var bibbase_data = {"data":"<img src=\"//bibbase.org/img/ajax-loader.gif\" id=\"spinner\"\n  style=\"display: none;\" alt=\"Loading..\" />\n\n<div id=\"bibbase\">\n\n  <script type=\"text/javascript\">\n    var bibbase = {\n      params: {\"bib\":\"http://www.eecs.qmul.ac.uk/~william/citations-ref.bib\",\"jsonp\":\"1\",\"host\":\"bibbase.org\"},\n      data: [{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Zane\",\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tai\"],\"firstnames\":[\"Nigel\",\"R.\",\"M.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D.\",\"William\",\"R.\"],\"suffixes\":[]}],\"date-added\":\"2019-03-09 15:35:24 +0000\",\"date-modified\":\"2019-03-09 15:35:24 +0000\",\"doi\":\"10.1007/s10115-016-0932-1\",\"issn\":\"0219-3116\",\"journal\":\"Knowledge and Information Systems\",\"month\":\"jan\",\"pages\":\"1–27\",\"title\":\"Clinical evidence framework for Bayesian networks\",\"year\":\"2017\",\"bdsk-url-1\":\"http://dx.doi.org/10.1007/s10115-016-0932-1\",\"bibtex\":\"@article{Yet2017,\\n\\tAbstract = {There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.},\\n\\tAuthor = {Yet, Barbaros and Perkins, Zane B. and Tai, Nigel R. M. and Marsh, D. William R.},\\n\\tDate-Added = {2019-03-09 15:35:24 +0000},\\n\\tDate-Modified = {2019-03-09 15:35:24 +0000},\\n\\tDoi = {10.1007/s10115-016-0932-1},\\n\\tIssn = {0219-3116},\\n\\tJournal = {Knowledge and Information Systems},\\n\\tMonth = {jan},\\n\\tPages = {1--27},\\n\\tTitle = {Clinical evidence framework for Bayesian networks},\\n\\tYear = {2017},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1007/s10115-016-0932-1}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Perkins, Z. B.\",\"Tai, N. R. M.\",\"Marsh, D. W. R.\"],\"key\":\"Yet2017\",\"id\":\"Yet2017\",\"bibbaseid\":\"yet-perkins-tai-marsh-clinicalevidenceframeworkforbayesiannetworks-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D.\",\"W.\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Zhang\"],\"firstnames\":[\"H.\"],\"suffixes\":[]}],\"booktitle\":\"Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)\",\"date-added\":\"2019-03-09 15:34:48 +0000\",\"date-modified\":\"2019-03-09 15:34:48 +0000\",\"editor\":[{\"firstnames\":[\"Lesley\"],\"propositions\":[],\"lastnames\":[\"Walls\"],\"suffixes\":[]},{\"firstnames\":[\"Matthew\"],\"propositions\":[],\"lastnames\":[\"Revie\"],\"suffixes\":[]},{\"firstnames\":[\"Tim\"],\"propositions\":[],\"lastnames\":[\"Bedford\"],\"suffixes\":[]}],\"month\":\"Sep\",\"pages\":\"1056-1063\",\"publisher\":\"CRC Press Reference - 486 Pages CRC Press ISBN 9781138029972\",\"title\":\"Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgement\",\"year\":\"2017\",\"bibtex\":\"@inproceedings{Marsh:2017aa,\\n\\tAbstract = {To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.},\\n\\tAuthor = {Marsh, D. W. R. and Zhang, H.},\\n\\tBooktitle = {Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)},\\n\\tDate-Added = {2019-03-09 15:34:48 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:48 +0000},\\n\\tEditor = {Lesley Walls and Matthew Revie and Tim Bedford},\\n\\tMonth = {Sep},\\n\\tPages = {1056-1063},\\n\\tPublisher = {CRC Press Reference - 486 Pages CRC Press ISBN 9781138029972},\\n\\tTitle = {{Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgement}},\\n\\tYear = {2017}}\\n\\n\",\"author_short\":[\"Marsh, D. W. R.\",\"Zhang, H.\"],\"editor_short\":[\"Walls, L.\",\"Revie, M.\",\"Bedford, T.\"],\"key\":\"Marsh:2017aa\",\"id\":\"Marsh:2017aa\",\"bibbaseid\":\"marsh-zhang-bayesiannetworkmodelsformakingmaintenancedecisionsfromdataandexpertjudgement-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"misc\",\"type\":\"misc\",\"abstract\":\"Recent research with middle school and university students highlights two factors that contribute to programming success: 1) understanding the level of abstraction that you are working at, and 2) being able to move between levels. In this qualitative study, we explored levels of abstraction, and particularly the design level,with five K-5 teachers. Here we outline 11 main findings. The teachers interviewed use the design level for both programming and writing. However, the two expert computing teachers have a far greater depth of understanding of the opportunities for the use of the design level, supporting pupils to understand the level they are working at and helping them move between levels of abstraction by using designs in novel ways. Further work is needed to investigate whether our results are generalisable. Further exploration of levels of abstraction and particularly how the design level helps K-5 learners learn to program, in the same way, that planning supports novices learning to write, is warranted.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"WAITE\"],\"firstnames\":[\"JL\"],\"suffixes\":[]},{\"propositions\":[\"curzon\"],\"lastnames\":[],\"firstnames\":[\"P\"],\"suffixes\":[]},{\"propositions\":[\"marsh\"],\"lastnames\":[],\"firstnames\":[\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sentance\"],\"firstnames\":[\"S\"],\"suffixes\":[]}],\"conference\":\"WiPSCE 2017\",\"date-added\":\"2019-03-09 15:34:42 +0000\",\"date-modified\":\"2019-03-09 15:34:42 +0000\",\"day\":\"9\",\"keyword\":[\"Design\"],\"month\":\"Nov\",\"title\":\"K-5 Teachers' Uses of Levels of Abstraction Focusing on Design\",\"year\":\"2017\",\"bibtex\":\"@misc{waite2017k5design,\\n\\tAbstract = {Recent research with middle school and university students highlights two factors that contribute to programming success: 1) understanding the level of abstraction that you are working at, and 2) being able to move between levels. In this qualitative study, we explored levels of abstraction, and particularly the design level,with five K-5 teachers. Here we outline 11 main findings. The teachers interviewed use the design level for both programming and writing. However, the two expert computing teachers have a far greater depth of understanding of the opportunities for the use of the design level, supporting pupils to understand the level they are\\nworking at and helping them move between levels of abstraction by using designs in novel ways. Further work is needed to investigate whether our results are generalisable. Further exploration of levels of abstraction and particularly how the design level helps K-5 learners learn to program, in the same way, that planning supports novices learning to write, is warranted.},\\n\\tAuthor = {WAITE, JL and curzon, P and marsh, D and Sentance, S},\\n\\tConference = {WiPSCE 2017},\\n\\tDate-Added = {2019-03-09 15:34:42 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:42 +0000},\\n\\tDay = {9},\\n\\tKeyword = {Design},\\n\\tMonth = {Nov},\\n\\tTitle = {K-5 Teachers' Uses of Levels of Abstraction Focusing on Design},\\n\\tYear = {2017}}\\n\\n\",\"author_short\":[\"WAITE, J.\",\"curzon , P\",\"marsh , D\",\"Sentance, S\"],\"key\":\"waite2017k5design\",\"id\":\"waite2017k5design\",\"bibbaseid\":\"waite-curzon-marsh-sentance-k5teachersusesoflevelsofabstractionfocusingondesign-2017\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"B\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"W\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Morrissey\"],\"firstnames\":[\"D\"],\"suffixes\":[]}],\"date-added\":\"2019-03-09 15:34:38 +0000\",\"date-modified\":\"2019-03-09 15:34:38 +0000\",\"eissn\":\"1879-8365\",\"journal\":\"Stud Health Technol Inform\",\"keyword\":[\"musculoskeletal\"],\"language\":\"eng\",\"organization\":\"Netherlands\",\"pages\":\"175–179\",\"publicationstatus\":\"published\",\"title\":\"Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions.\",\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/30306931\",\"volume\":\"255\",\"year\":\"2018\",\"bdsk-url-1\":\"https://www.ncbi.nlm.nih.gov/pubmed/30306931\",\"bibtex\":\"@article{yet2018towardsconditions,\\n\\tAbstract = {Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.},\\n\\tAuthor = {Yet, B and Marsh, W and Morrissey, D},\\n\\tDate-Added = {2019-03-09 15:34:38 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:38 +0000},\\n\\tEissn = {1879-8365},\\n\\tJournal = {Stud Health Technol Inform},\\n\\tKeyword = {musculoskeletal},\\n\\tLanguage = {eng},\\n\\tOrganization = {Netherlands},\\n\\tPages = {175--179},\\n\\tPublicationstatus = {published},\\n\\tTitle = {Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions.},\\n\\tUrl = {https://www.ncbi.nlm.nih.gov/pubmed/30306931},\\n\\tVolume = {255},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/30306931}}\\n\\n\",\"author_short\":[\"Yet, B\",\"Marsh, W\",\"Morrissey, D\"],\"key\":\"yet2018towardsconditions\",\"id\":\"yet2018towardsconditions\",\"bibbaseid\":\"yet-marsh-morrissey-towardsanevidencebaseddecisionsupporttoolformanagementofmusculoskeletalconditions-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ncbi.nlm.nih.gov/pubmed/30306931\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess `do-ability'. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers' knowledge of terms including algorithm and code; a concept of `emergent algorithms' is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study's results are generalisable more widely.\",\"address\":\"On line Journal\",\"author\":[{\"propositions\":[],\"lastnames\":[\"WAITE\"],\"firstnames\":[\"JL\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"CURZON\"],\"firstnames\":[\"P\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"MARSH\"],\"firstnames\":[\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sentance\"],\"firstnames\":[\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hawden-Bennett\"],\"firstnames\":[\"A\"],\"suffixes\":[]}],\"date-added\":\"2019-03-09 15:34:34 +0000\",\"date-modified\":\"2019-03-09 15:34:34 +0000\",\"day\":\"31\",\"doi\":\"10.21585/ijcses.v2i1.23\",\"editor\":[{\"propositions\":[],\"lastnames\":[\"Kalelioglu\"],\"firstnames\":[\"F\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Allsop\"],\"firstnames\":[\"Y\"],\"suffixes\":[]}],\"issn\":\"2513-8359\",\"issue\":\"1\",\"journal\":\"International Journal Of Computer Science Education In Schools\",\"keyword\":[\"Computing Eductation\"],\"month\":\"Jan\",\"number\":\"2\",\"pages\":\"14–40\",\"publicationstatus\":\"published\",\"title\":\"Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming\",\"url\":\"http://www.ijcses.org/index.php/ijcses/index\",\"volume\":\"2\",\"year\":\"2018\",\"bdsk-url-1\":\"http://www.ijcses.org/index.php/ijcses/index\",\"bdsk-url-2\":\"https://doi.org/10.21585/ijcses.v2i1.23\",\"bibtex\":\"@article{waite2018abstractionprogramming,\\n\\tAbstract = {Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess `do-ability'. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers'\\nknowledge of terms including algorithm and code; a concept of `emergent algorithms' is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study's results are generalisable more widely.},\\n\\tAddress = {On line Journal},\\n\\tAuthor = {WAITE, JL and CURZON, P and MARSH, D and Sentance, S and Hawden-Bennett, A},\\n\\tDate-Added = {2019-03-09 15:34:34 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:34 +0000},\\n\\tDay = {31},\\n\\tDoi = {10.21585/ijcses.v2i1.23},\\n\\tEditor = {Kalelioglu, F and Allsop, Y},\\n\\tIssn = {2513-8359},\\n\\tIssue = {1},\\n\\tJournal = {International Journal Of Computer Science Education In Schools},\\n\\tKeyword = {Computing Eductation},\\n\\tMonth = {Jan},\\n\\tNumber = {2},\\n\\tPages = {14--40},\\n\\tPublicationstatus = {published},\\n\\tTitle = {Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming},\\n\\tUrl = {http://www.ijcses.org/index.php/ijcses/index},\\n\\tVolume = {2},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {http://www.ijcses.org/index.php/ijcses/index},\\n\\tBdsk-Url-2 = {https://doi.org/10.21585/ijcses.v2i1.23}}\\n\\n\",\"author_short\":[\"WAITE, J.\",\"CURZON, P\",\"MARSH, D\",\"Sentance, S\",\"Hawden-Bennett, A\"],\"editor_short\":[\"Kalelioglu, F\",\"Allsop, Y\"],\"key\":\"waite2018abstractionprogramming\",\"id\":\"waite2018abstractionprogramming\",\"bibbaseid\":\"waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.ijcses.org/index.php/ijcses/index\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"McLachlan\"],\"firstnames\":[\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dube\"],\"firstnames\":[\"K\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Buchanan\"],\"firstnames\":[\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lean\"],\"firstnames\":[\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Johnson\"],\"firstnames\":[\"O\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Potts\"],\"firstnames\":[\"H\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gallagher\"],\"firstnames\":[\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"W\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"N\"],\"suffixes\":[]}],\"date-added\":\"2019-03-09 15:34:29 +0000\",\"date-modified\":\"2019-03-09 15:34:29 +0000\",\"day\":\"27\",\"doi\":\"10.14236/jhi.v25i1.981\",\"eissn\":\"2058-4563\",\"issue\":\"1\",\"journal\":\"J Innov Health Inform\",\"keyword\":[\"Learning Health Systems\",\" Electronic Health Records\"],\"language\":\"eng\",\"month\":\"Mar\",\"organization\":\"England\",\"pages\":\"981\",\"publicationstatus\":\"online-published\",\"title\":\"Learning Health Systems: The research community awareness challenge.\",\"url\":\"https://www.ncbi.nlm.nih.gov/pubmed/29717954\",\"volume\":\"25\",\"year\":\"2018\",\"bdsk-url-1\":\"https://www.ncbi.nlm.nih.gov/pubmed/29717954\",\"bdsk-url-2\":\"https://doi.org/10.14236/jhi.v25i1.981\",\"bibtex\":\"@article{mclachlan2018learningchallenge,\\n\\tAuthor = {McLachlan, S and Dube, K and Buchanan, D and Lean, S and Johnson, O and Potts, H and Gallagher, T and Marsh, W and Fenton, N},\\n\\tDate-Added = {2019-03-09 15:34:29 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:29 +0000},\\n\\tDay = {27},\\n\\tDoi = {10.14236/jhi.v25i1.981},\\n\\tEissn = {2058-4563},\\n\\tIssue = {1},\\n\\tJournal = {J Innov Health Inform},\\n\\tKeyword = {Learning Health Systems, Electronic Health Records},\\n\\tLanguage = {eng},\\n\\tMonth = {Mar},\\n\\tOrganization = {England},\\n\\tPages = {981},\\n\\tPublicationstatus = {online-published},\\n\\tTitle = {Learning Health Systems: The research community awareness challenge.},\\n\\tUrl = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},\\n\\tVolume = {25},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},\\n\\tBdsk-Url-2 = {https://doi.org/10.14236/jhi.v25i1.981}}\\n\\n\",\"author_short\":[\"McLachlan, S\",\"Dube, K\",\"Buchanan, D\",\"Lean, S\",\"Johnson, O\",\"Potts, H\",\"Gallagher, T\",\"Marsh, W\",\"Fenton, N\"],\"key\":\"mclachlan2018learningchallenge\",\"id\":\"mclachlan2018learningchallenge\",\"bibbaseid\":\"mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.ncbi.nlm.nih.gov/pubmed/29717954\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"ZHANG\"],\"firstnames\":[\"H\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"MARSH\"],\"firstnames\":[\"DWR\"],\"suffixes\":[]}],\"booktitle\":\"ESREL2018 conference proceeding\",\"conference\":\"European Safety and Reliability Conference ESREL 2018\",\"date-added\":\"2019-03-09 15:34:26 +0000\",\"date-modified\":\"2019-03-09 15:34:26 +0000\",\"day\":\"17\",\"finishday\":\"21\",\"finishmonth\":\"Jun\",\"finishyear\":\"2018\",\"keyword\":[\"probabilistic relational model\"],\"month\":\"Jun\",\"organization\":\"Trondheim, Norway\",\"publicationstatus\":\"accepted\",\"publisher\":\"Taylor & Francis\",\"startday\":\"17\",\"startmonth\":\"Jun\",\"startyear\":\"2018\",\"title\":\"Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models\",\"url\":\"http://haoyuan.uk/\",\"year\":\"2018\",\"bdsk-url-1\":\"http://haoyuan.uk/\",\"bibtex\":\"@inproceedings{zhang2018towardsmodels,\\n\\tAbstract = {Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.},\\n\\tAuthor = {ZHANG, H and MARSH, DWR},\\n\\tBooktitle = {ESREL2018 conference proceeding},\\n\\tConference = {European Safety and Reliability Conference ESREL 2018},\\n\\tDate-Added = {2019-03-09 15:34:26 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:26 +0000},\\n\\tDay = {17},\\n\\tFinishday = {21},\\n\\tFinishmonth = {Jun},\\n\\tFinishyear = {2018},\\n\\tKeyword = {probabilistic relational model},\\n\\tMonth = {Jun},\\n\\tOrganization = {Trondheim, Norway},\\n\\tPublicationstatus = {accepted},\\n\\tPublisher = {Taylor \\\\& Francis},\\n\\tStartday = {17},\\n\\tStartmonth = {Jun},\\n\\tStartyear = {2018},\\n\\tTitle = {Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models},\\n\\tUrl = {http://haoyuan.uk/},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {http://haoyuan.uk/}}\\n\\n\",\"author_short\":[\"ZHANG, H\",\"MARSH, D.\"],\"key\":\"zhang2018towardsmodels\",\"id\":\"zhang2018towardsmodels\",\"bibbaseid\":\"zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://haoyuan.uk/\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"There are many proposed benefits of using learning health systems (LHS), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS are identified and classified. As a result, the LHS `community' is fragmented, with groups working on similar systems being not aware of each other's work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption. Objective To find a way to support easy identification and classification of research works within the domain of LHS. Method A qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework. Results The study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain. Conclusion Our finding brings clarification where there has been limited aware- ness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework's effect on the LHS domain.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"MCLACHLAN\"],\"firstnames\":[\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Potts\"],\"firstnames\":[\"HWW\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Dube\"],\"firstnames\":[\"K\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Buchanan\"],\"firstnames\":[\"D\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lean\"],\"firstnames\":[\"S\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Gallagher\"],\"firstnames\":[\"T\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Johnson\"],\"firstnames\":[\"O\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"DALEY\"],\"firstnames\":[\"B\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"W\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"FENTON\"],\"firstnames\":[\"N\"],\"suffixes\":[]}],\"date-added\":\"2019-03-09 15:34:21 +0000\",\"date-modified\":\"2019-03-09 15:34:21 +0000\",\"day\":\"15\",\"doi\":\"10.14236/jhi.v25i2.996\",\"issue\":\"2\",\"journal\":\"BCS Journal of Innovation in Health Informatics\",\"keyword\":[\"precision medicine\"],\"month\":\"Jun\",\"publicationstatus\":\"published\",\"title\":\"The Heimdall framework for supporting characterisation of learning health systems\",\"volume\":\"25\",\"year\":\"2018\",\"bdsk-url-1\":\"https://doi.org/10.14236/jhi.v25i2.996\",\"bibtex\":\"@article{mclachlan2018thesystems,\\n\\tAbstract = {There are many proposed benefits of using learning health systems  (LHS), including improved patient outcomes. There has been little adoption of LHS  in  practice  due  to  challenges  and  barriers  that  limit  adoption  of  new  data-driven  technologies  in  healthcare.  We  have  identified  a  more  fundamental  explanation:  the  majority  of  developments  in  LHS  are  not  identified  as  LHS.  The  absence  of  a  unifying  namespace  and  framework  brings  a  lack  of  consistency  in  how  LHS  are identified and classified. As a result, the LHS `community' is fragmented, with  groups working on similar systems being not aware of each other's work. This leads  to duplication and the lack of a critical mass of researchers necessary to address  barriers to adoption.\\nObjective  To  find  a  way  to  support  easy  identification  and  classification  of  research works within the domain of LHS.\\nMethod  A qualitative meta-narrative study focusing on works that self-identified  as LHS was used for two purposes. First, to find existing standard definitions and  frameworks  using  these  to  create  a  new  unifying  framework.  Second,  seeking  whether it was possible to classify those LHS solutions within the new framework. Results  The study found that with apparently limited awareness, all current LHS  works  fall  within  nine  primary  archetypes.  These  findings  were  used  to  develop a  unifying  framework  for  LHS  to  classify  works  as  LHS,  and  reduce  diversity  and  fragmentation within the domain.\\nConclusion  Our finding brings clarification where there has been limited aware- ness  for  LHS  among  researchers.  We  believe  our  framework  is  simple  and  may  help researchers to classify works in the LHS domain. This framework may enable  realisation  of  the  critical  mass  necessary  to  bring  more  substantial  collaboration  and funding to LHS. Ongoing research will seek to establish the framework's effect  on the LHS domain.},\\n\\tAuthor = {MCLACHLAN, S and Potts, HWW and Dube, K and Buchanan, D and Lean, S and Gallagher, T and Johnson, O and DALEY, B and Marsh, W and FENTON, N},\\n\\tDate-Added = {2019-03-09 15:34:21 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:21 +0000},\\n\\tDay = {15},\\n\\tDoi = {10.14236/jhi.v25i2.996},\\n\\tIssue = {2},\\n\\tJournal = {BCS Journal of Innovation in Health Informatics},\\n\\tKeyword = {precision medicine},\\n\\tMonth = {Jun},\\n\\tPublicationstatus = {published},\\n\\tTitle = {The Heimdall framework for supporting characterisation of learning health systems},\\n\\tVolume = {25},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {https://doi.org/10.14236/jhi.v25i2.996}}\\n\\n\",\"author_short\":[\"MCLACHLAN, S\",\"Potts, H.\",\"Dube, K\",\"Buchanan, D\",\"Lean, S\",\"Gallagher, T\",\"Johnson, O\",\"DALEY, B\",\"Marsh, W\",\"FENTON, N\"],\"key\":\"mclachlan2018thesystems\",\"id\":\"mclachlan2018thesystems\",\"bibbaseid\":\"mclachlan-potts-dube-buchanan-lean-gallagher-johnson-daley-etal-theheimdallframeworkforsupportingcharacterisationoflearninghealthsystems-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"K-5 teachers teach a range of subjects & develop generic teaching skills; when starting to teach computing, particularly programming, practitioners may not realise that they can draw on these other skills to support their teaching. In a small study of K-5 teachers, potential synergies were suggested between using planning in the the teaching of writing and design in the teaching of programming. In this paper, we explore these synergies by surveying a wider group of teachers (n=207) on their uses of planning and design. Teachers reported the usefulness of planning for writing and design for programming as equally important. However, there were significant differences in their uses. The majority saw planning as essential in writing & put this into practice in their teaching. For example, they demonstrated the creation of plans, expected students to annotate plans, required students to refer to plans when writing and used plans to differentiate. By contrast, these uses were implemented less frequently in programming tasks. We also report on differences in the confidence of male & female respondents, & between generalists (who teach programming & writing) & specialists (who do not teach writing). For example, females were more confident to teach writing than programming, with males vice versa. Having revealed opportunities for reuse of successful techniques used in teaching writing for the teaching of programming we recommend further work is needed to explore this transfer of pedagogical knowledge.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"WAITE\"],\"firstnames\":[\"JL\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"CURZON\"],\"firstnames\":[\"P\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"MARSH\"],\"firstnames\":[\"DW\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Sentance\"],\"firstnames\":[\"S\"],\"suffixes\":[]}],\"booktitle\":\"ACM ISBN 978-1-4503-6588-8/18/10\",\"conference\":\"WiPSCE 2018 (13th Workshop in Primary and Secondary Computing Education)\",\"date-added\":\"2019-03-09 15:34:15 +0000\",\"date-modified\":\"2019-03-09 15:34:15 +0000\",\"day\":\"4\",\"doi\":\"10.1145/3265757.3265761\",\"editor\":[{\"propositions\":[],\"lastnames\":[\"Muhling\"],\"firstnames\":[\"A\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cutts\"],\"firstnames\":[\"Q\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Schwill\"],\"firstnames\":[\"A\"],\"suffixes\":[]}],\"finishday\":\"6\",\"finishmonth\":\"Oct\",\"finishyear\":\"2018\",\"howpublished\":\"Online\",\"isbn\":\"978-1-4503-6588-8\",\"keyword\":[\"Design\"],\"month\":\"Oct\",\"organization\":\"Potsdam, Germany\",\"publicationstatus\":\"accepted\",\"startday\":\"4\",\"startmonth\":\"Oct\",\"startyear\":\"2018\",\"title\":\"Comparing K-5 teachers' reported use of design in teaching programming and planning in teaching writing\",\"year\":\"2018\",\"bdsk-url-1\":\"https://doi.org/10.1145/3265757.3265761\",\"bibtex\":\"@inproceedings{waite2018comparingwriting,\\n\\tAbstract = {K-5 teachers teach a range of subjects \\\\& develop generic teaching\\nskills; when starting to teach computing, particularly programming,\\npractitioners may not realise that they can draw on these other\\nskills to support their teaching. In a small study of K-5 teachers,\\npotential synergies were suggested between using planning in the\\nthe teaching of writing and design in the teaching of programming.\\nIn this paper, we explore these synergies by surveying a wider group\\nof teachers (n=207) on their uses of planning and design. Teachers\\nreported the usefulness of planning for writing and design for\\nprogramming as equally important. However, there were significant\\ndifferences in their uses. The majority saw planning as essential in\\nwriting \\\\& put this into practice in their teaching. For example, they\\ndemonstrated the creation of plans, expected students to annotate\\nplans, required students to refer to plans when writing and used\\nplans to differentiate. By contrast, these uses were implemented less\\nfrequently in programming tasks. We also report on differences in\\nthe confidence of male \\\\& female respondents, \\\\& between generalists\\n(who teach programming \\\\& writing) \\\\& specialists (who do not\\nteach writing). For example, females were more confident to teach\\nwriting than programming, with males vice versa. Having revealed\\nopportunities for reuse of successful techniques used in teaching\\nwriting for the teaching of programming we recommend further\\nwork is needed to explore this transfer of pedagogical knowledge.},\\n\\tAuthor = {WAITE, JL and CURZON, P and MARSH, DW and Sentance, S},\\n\\tBooktitle = {ACM ISBN 978-1-4503-6588-8/18/10},\\n\\tConference = {WiPSCE 2018 (13th Workshop in Primary and Secondary Computing Education)},\\n\\tDate-Added = {2019-03-09 15:34:15 +0000},\\n\\tDate-Modified = {2019-03-09 15:34:15 +0000},\\n\\tDay = {4},\\n\\tDoi = {10.1145/3265757.3265761},\\n\\tEditor = {Muhling, A and Cutts, Q and Schwill, A},\\n\\tFinishday = {6},\\n\\tFinishmonth = {Oct},\\n\\tFinishyear = {2018},\\n\\tHowpublished = {Online},\\n\\tIsbn = {978-1-4503-6588-8},\\n\\tKeyword = {Design},\\n\\tMonth = {Oct},\\n\\tOrganization = {Potsdam, Germany},\\n\\tPublicationstatus = {accepted},\\n\\tStartday = {4},\\n\\tStartmonth = {Oct},\\n\\tStartyear = {2018},\\n\\tTitle = {Comparing K-5 teachers' reported use of design in teaching programming and planning in teaching writing},\\n\\tYear = {2018},\\n\\tBdsk-Url-1 = {https://doi.org/10.1145/3265757.3265761}}\\n\\n\",\"author_short\":[\"WAITE, J.\",\"CURZON, P\",\"MARSH, D.\",\"Sentance, S\"],\"editor_short\":[\"Muhling, A\",\"Cutts, Q\",\"Schwill, A\"],\"key\":\"waite2018comparingwriting\",\"id\":\"waite2018comparingwriting\",\"bibbaseid\":\"waite-curzon-marsh-sentance-comparingk5teachersreporteduseofdesigninteachingprogrammingandplanninginteachingwriting-2018\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support. \",\"author\":[{\"propositions\":[],\"lastnames\":[\"Kyrimi\"],\"firstnames\":[\"Evangelia\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of the Eighth International Conference on Probabilistic Graphical Models\",\"date-added\":\"2016-11-05 18:14:43 +0000\",\"date-modified\":\"2016-11-05 23:26:54 +0000\",\"pages\":\"275–286\",\"title\":\"A Progressive Explanation of Inference in `Hybrid' Bayesian Networks for Supporting Clinical Decision Making\",\"url\":\"http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf\",\"year\":\"2016\",\"bdsk-url-1\":\"http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf\",\"bibtex\":\"@inproceedings{Evangelia-Kyrimi:2016wd,\\n\\tAbstract = {Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support.\\n},\\n\\tAuthor = {Kyrimi, Evangelia and Marsh, William},\\n\\tBooktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},\\n\\tDate-Added = {2016-11-05 18:14:43 +0000},\\n\\tDate-Modified = {2016-11-05 23:26:54 +0000},\\n\\tPages = {275--286},\\n\\tTitle = {A Progressive Explanation of Inference in `Hybrid' Bayesian Networks for Supporting Clinical Decision Making},\\n\\tUrl = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf},\\n\\tYear = {2016},\\n\\tBdsk-Url-1 = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf}}\\n\\n\",\"author_short\":[\"Kyrimi, E.\",\"Marsh, W.\"],\"key\":\"Evangelia-Kyrimi:2016wd\",\"id\":\"Evangelia-Kyrimi:2016wd\",\"bibbaseid\":\"kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Background Lower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT. Methods A systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor. Results Forty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation. Conclusion A significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling. \",\"author\":[{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Z.\",\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"B.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Glasgow\"],\"firstnames\":[\"S.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cole\"],\"firstnames\":[\"E.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"W.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Brohi\"],\"firstnames\":[\"K.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rasmussen\"],\"firstnames\":[\"T.\",\"E.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tai\"],\"firstnames\":[\"N.\",\"R.\",\"M.\"],\"suffixes\":[]}],\"date-added\":\"2016-11-05 17:30:11 +0000\",\"date-modified\":\"2016-11-05 23:33:25 +0000\",\"doi\":\"10.1002/bjs.9689\",\"issn\":\"1365-2168\",\"journal\":\"British Journal of Surgery\",\"number\":\"5\",\"pages\":\"436–450\",\"publisher\":\"John Wiley & Sons, Ltd\",\"title\":\"Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma\",\"url\":\"https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf\",\"volume\":\"102\",\"year\":\"2015\",\"bdsk-url-1\":\"http://dx.doi.org/10.1002/bjs.9689\",\"bdsk-url-2\":\"https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf\",\"bibtex\":\"@article{Perkins2015,\\n\\tAbstract = {\\nBackground\\nLower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT.\\n\\nMethods\\nA systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor.\\n\\nResults\\nForty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation.\\n\\nConclusion\\nA significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling.\\n},\\n\\tAuthor = {Perkins, Z. B. and Yet, B. and Glasgow, S. and Cole, E. and Marsh, W. and Brohi, K. and Rasmussen, T. E. and Tai, N. R. M.},\\n\\tDate-Added = {2016-11-05 17:30:11 +0000},\\n\\tDate-Modified = {2016-11-05 23:33:25 +0000},\\n\\tDoi = {10.1002/bjs.9689},\\n\\tIssn = {1365-2168},\\n\\tJournal = {British Journal of Surgery},\\n\\tNumber = {5},\\n\\tPages = {436--450},\\n\\tPublisher = {John Wiley & Sons, Ltd},\\n\\tTitle = {Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma},\\n\\tUrl = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf},\\n\\tVolume = {102},\\n\\tYear = {2015},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1002/bjs.9689},\\n\\tBdsk-Url-2 = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf}}\\n\\n\",\"author_short\":[\"Perkins, Z. B.\",\"Yet, B.\",\"Glasgow, S.\",\"Cole, E.\",\"Marsh, W.\",\"Brohi, K.\",\"Rasmussen, T. E.\",\"Tai, N. R. M.\"],\"key\":\"Perkins2015\",\"id\":\"Perkins2015\",\"bibbaseid\":\"perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Abstract We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed `solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original \\\\BN\\\\ nodes unchanged. \",\"author\":[{\"firstnames\":[\"Norman\"],\"propositions\":[],\"lastnames\":[\"Fenton\"],\"suffixes\":[]},{\"firstnames\":[\"Martin\"],\"propositions\":[],\"lastnames\":[\"Neil\"],\"suffixes\":[]},{\"firstnames\":[\"David\"],\"propositions\":[],\"lastnames\":[\"Lagnado\"],\"suffixes\":[]},{\"firstnames\":[\"William\"],\"propositions\":[],\"lastnames\":[\"Marsh\"],\"suffixes\":[]},{\"firstnames\":[\"Barbaros\"],\"propositions\":[],\"lastnames\":[\"Yet\"],\"suffixes\":[]},{\"firstnames\":[\"Anthony\"],\"propositions\":[],\"lastnames\":[\"Constantinou\"],\"suffixes\":[]}],\"date-added\":\"2016-10-24 22:12:29 +0000\",\"date-modified\":\"2016-11-05 23:32:43 +0000\",\"doi\":\"10.1016/j.knosys.2016.09.012\",\"issn\":\"0950-7051\",\"journal\":\"Knowledge-Based Systems\",\"keywords\":\"Uncertain reasoning\",\"pages\":\"39 - 50\",\"title\":\"How to model mutually exclusive events based on independent causal pathways in Bayesian network models\",\"url\":\"http://www.sciencedirect.com/science/article/pii/S095070511630329X\",\"volume\":\"113\",\"year\":\"2016\",\"bdsk-url-1\":\"http://www.sciencedirect.com/science/article/pii/S095070511630329X\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.knosys.2016.09.012\",\"bibtex\":\"@article{Fenton201639,\\n\\tAbstract = {Abstract We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed `solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original \\\\{BN\\\\} nodes unchanged. },\\n\\tAuthor = {Norman Fenton and Martin Neil and David Lagnado and William Marsh and Barbaros Yet and Anthony Constantinou},\\n\\tDate-Added = {2016-10-24 22:12:29 +0000},\\n\\tDate-Modified = {2016-11-05 23:32:43 +0000},\\n\\tDoi = {10.1016/j.knosys.2016.09.012},\\n\\tIssn = {0950-7051},\\n\\tJournal = {Knowledge-Based Systems},\\n\\tKeywords = {Uncertain reasoning},\\n\\tPages = {39 - 50},\\n\\tTitle = {How to model mutually exclusive events based on independent causal pathways in Bayesian network models},\\n\\tUrl = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},\\n\\tVolume = {113},\\n\\tYear = {2016},\\n\\tBdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.knosys.2016.09.012}}\\n\\n\",\"author_short\":[\"Fenton, N.\",\"Neil, M.\",\"Lagnado, D.\",\"Marsh, W.\",\"Yet, B.\",\"Constantinou, A.\"],\"key\":\"Fenton201639\",\"id\":\"Fenton201639\",\"bibbaseid\":\"fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://www.sciencedirect.com/science/article/pii/S095070511630329X\"},\"keyword\":[\"Uncertain reasoning\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"In many organisations, large databases are created as part of the business operation: the promise of ?big data? is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts? knowledge is vital for the model?s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.\",\"annote\":\"doi: 10.1080/09617353.2016.1148923\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Nur\"],\"firstnames\":[\"Khalid\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Majumdar\"],\"firstnames\":[\"Arnab\"],\"suffixes\":[]}],\"booktitle\":\"Safety and Reliability\",\"date\":\"2016/01/02\",\"date-added\":\"2016-10-24 22:06:10 +0000\",\"date-modified\":\"2016-11-05 23:32:15 +0000\",\"doi\":\"10.1080/09617353.2016.1148923\",\"isbn\":\"0961-7353\",\"journal\":\"Safety and Reliability\",\"journal1\":\"Safety and Reliability\",\"month\":\"jan\",\"number\":\"1\",\"pages\":\"35–47\",\"publisher\":\"Taylor & Francis\",\"title\":\"Using operational data for decision making: a feasibility study in rail maintenance\",\"url\":\"http://dx.doi.org/10.1080/09617353.2016.1148923\",\"volume\":\"36\",\"year\":\"2016\",\"year1\":\"2016\",\"bdsk-url-1\":\"http://dx.doi.org/10.1080/09617353.2016.1148923\",\"bibtex\":\"@article{Marsh:2016nr,\\n\\tAbstract = {In many organisations, large databases are created as part of the business operation: the promise of ?big data? is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts? knowledge is vital for the model?s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.},\\n\\tAnnote = {doi: 10.1080/09617353.2016.1148923},\\n\\tAuthor = {Marsh, William and Nur, Khalid and Yet, Barbaros and Majumdar, Arnab},\\n\\tBooktitle = {Safety and Reliability},\\n\\tDate = {2016/01/02},\\n\\tDate-Added = {2016-10-24 22:06:10 +0000},\\n\\tDate-Modified = {2016-11-05 23:32:15 +0000},\\n\\tDoi = {10.1080/09617353.2016.1148923},\\n\\tIsbn = {0961-7353},\\n\\tJournal = {Safety and Reliability},\\n\\tJournal1 = {Safety and Reliability},\\n\\tMonth = {jan},\\n\\tNumber = {1},\\n\\tPages = {35--47},\\n\\tPublisher = {Taylor \\\\& Francis},\\n\\tTitle = {Using operational data for decision making: a feasibility study in rail maintenance},\\n\\tUrl = {http://dx.doi.org/10.1080/09617353.2016.1148923},\\n\\tVolume = {36},\\n\\tYear = {2016},\\n\\tYear1 = {2016},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1080/09617353.2016.1148923}}\\n\\n\",\"author_short\":[\"Marsh, W.\",\"Nur, K.\",\"Yet, B.\",\"Majumdar, A.\"],\"key\":\"Marsh:2016nr\",\"id\":\"Marsh:2016nr\",\"bibbaseid\":\"marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"http://dx.doi.org/10.1080/09617353.2016.1148923\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D.\",\"William\",\"R.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\",\"J.\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of the Ninth International Scientific School MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg, Russia)\",\"date-added\":\"2016-02-20 17:11:29 +0000\",\"date-modified\":\"2016-02-20 17:14:07 +0000\",\"month\":\"July\",\"title\":\"Why Risk Models should be Parameterised.\",\"year\":\"2009\",\"bibtex\":\"@inproceedings{Marsh2009,\\n\\tAuthor = {Marsh, D. William R. and Bearfield, George J.},\\n\\tBooktitle = {Proceedings of the Ninth International Scientific School MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg, Russia)},\\n\\tDate-Added = {2016-02-20 17:11:29 +0000},\\n\\tDate-Modified = {2016-02-20 17:14:07 +0000},\\n\\tMonth = {July},\\n\\tTitle = {Why Risk Models should be Parameterised.},\\n\\tYear = {2009}}\\n\\n\",\"author_short\":[\"Marsh, D. W. R.\",\"Bearfield, G. J.\"],\"key\":\"Marsh2009\",\"id\":\"Marsh2009\",\"bibbaseid\":\"marsh-bearfield-whyriskmodelsshouldbeparameterised-2009\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Dray\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\",\"J.\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D\",\"William\",\"R.\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of 25th International System Safety Conference, Baltimore, USA\",\"date-added\":\"2016-02-20 16:34:41 +0000\",\"date-modified\":\"2016-11-06 19:59:37 +0000\",\"month\":\"August\",\"title\":\"Constructing Scalable and Parameterised System Wide Risk Models\",\"year\":\"2007\",\"bibtex\":\"@inproceedings{Dray2007,\\n\\tAuthor = {Dray, Peter and Bearfield, George J. and Marsh, D William R.},\\n\\tBooktitle = {Proceedings of 25th International System Safety Conference, Baltimore, USA},\\n\\tDate-Added = {2016-02-20 16:34:41 +0000},\\n\\tDate-Modified = {2016-11-06 19:59:37 +0000},\\n\\tMonth = {August},\\n\\tTitle = {Constructing Scalable and Parameterised System Wide Risk Models},\\n\\tYear = {2007}}\\n\\n\",\"author_short\":[\"Dray, P.\",\"Bearfield, G. J.\",\"Marsh, D W. R.\"],\"key\":\"Dray2007\",\"id\":\"Dray2007\",\"bibbaseid\":\"dray-bearfield-marsh-constructingscalableandparameterisedsystemwideriskmodels-2007\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Wichmann\"],\"firstnames\":[\"BA\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Canning\"],\"firstnames\":[\"AA\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Clutterbuck\"],\"firstnames\":[\"DL\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Winsborrow\"],\"firstnames\":[\"LA\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Ward\"],\"firstnames\":[\"NJ\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"DWR\"],\"suffixes\":[]}],\"journal\":\"Software Engineering Journal\",\"number\":\"2\",\"pages\":\"69\",\"publisher\":\"London: published by the Institution of Electrical Engineers for the British Computer Society and the Institution of Electrical Engineers, c1986-c1996.\",\"title\":\"Industrial perspective on static analysis\",\"volume\":\"10\",\"year\":\"1995\",\"bibtex\":\"@article{wichmann1995industrial,\\n\\tAuthor = {Wichmann, BA and Canning, AA and Clutterbuck, DL and Winsborrow, LA and Ward, NJ and Marsh, DWR},\\n\\tJournal = {Software Engineering Journal},\\n\\tNumber = {2},\\n\\tPages = {69},\\n\\tPublisher = {London: published by the Institution of Electrical Engineers for the British Computer Society and the Institution of Electrical Engineers, c1986-c1996.},\\n\\tTitle = {Industrial perspective on static analysis},\\n\\tVolume = {10},\\n\\tYear = {1995}}\\n\\n\",\"author_short\":[\"Wichmann, B.\",\"Canning, A.\",\"Clutterbuck, D.\",\"Winsborrow, L.\",\"Ward, N.\",\"Marsh, D.\"],\"key\":\"wichmann1995industrial\",\"id\":\"wichmann1995industrial\",\"bibbaseid\":\"wichmann-canning-clutterbuck-winsborrow-ward-marsh-industrialperspectiveonstaticanalysis-1995\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"DWR\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"G\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-06 19:55:16 +0000\",\"doi\":\"10.1243/1748006XJRR131\",\"journal\":\"Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability\",\"number\":\"2\",\"pages\":\"105–114\",\"publisher\":\"SAGE Publications\",\"title\":\"Generalizing event trees using Bayesian networks\",\"volume\":\"222\",\"year\":\"2008\",\"bdsk-url-1\":\"http://dx.doi.org/10.1243/1748006XJRR131\",\"bibtex\":\"@article{marsh2008generalizing,\\n\\tAuthor = {Marsh, DWR and Bearfield, G},\\n\\tDate-Modified = {2016-11-06 19:55:16 +0000},\\n\\tDoi = {10.1243/1748006XJRR131},\\n\\tJournal = {Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability},\\n\\tNumber = {2},\\n\\tPages = {105--114},\\n\\tPublisher = {SAGE Publications},\\n\\tTitle = {Generalizing event trees using Bayesian networks},\\n\\tVolume = {222},\\n\\tYear = {2008},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1243/1748006XJRR131}}\\n\\n\",\"author_short\":[\"Marsh, D.\",\"Bearfield, G\"],\"key\":\"marsh2008generalizing\",\"id\":\"marsh2008generalizing\",\"bibbaseid\":\"marsh-bearfield-generalizingeventtreesusingbayesiannetworks-2008\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"booktitle\":\"Proceedings of 24th International Conference, SAFECOMP 2005, Computer Safety, Reliability, and Security\",\"date-modified\":\"2016-02-20 17:44:15 +0000\",\"pages\":\"52–66\",\"publisher\":\"Springer Berlin Heidelberg\",\"series\":\"Lecture Notes in Computer Science\",\"title\":\"Generalising event trees using Bayesian networks with a case study of train derailment\",\"volume\":\"3688\",\"year\":\"2005\",\"bibtex\":\"@incollection{bearfield2005generalising,\\n\\tAuthor = {Bearfield, George and Marsh, William},\\n\\tBooktitle = {Proceedings of 24th International Conference, SAFECOMP 2005, Computer Safety, Reliability, and Security},\\n\\tDate-Modified = {2016-02-20 17:44:15 +0000},\\n\\tPages = {52--66},\\n\\tPublisher = {Springer Berlin Heidelberg},\\n\\tSeries = {Lecture Notes in Computer Science},\\n\\tTitle = {Generalising event trees using Bayesian networks with a case study of train derailment},\\n\\tVolume = {3688},\\n\\tYear = {2005}}\\n\\n\",\"author_short\":[\"Bearfield, G.\",\"Marsh, W.\"],\"key\":\"bearfield2005generalising\",\"id\":\"bearfield2005generalising\",\"bibbaseid\":\"bearfield-marsh-generalisingeventtreesusingbayesiannetworkswithacasestudyoftrainderailment-2005\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"incollection\",\"type\":\"incollection\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\"],\"suffixes\":[]}],\"booktitle\":\"Probabilistic Safety Assessment and Management\",\"pages\":\"3597–3602\",\"publisher\":\"Springer London\",\"title\":\"Using Bayesian networks to model accident causation in the UK railway industry\",\"year\":\"2004\",\"bibtex\":\"@incollection{marsh2004using,\\n\\tAuthor = {Marsh, William and Bearfield, George},\\n\\tBooktitle = {Probabilistic Safety Assessment and Management},\\n\\tPages = {3597--3602},\\n\\tPublisher = {Springer London},\\n\\tTitle = {Using Bayesian networks to model accident causation in the UK railway industry},\\n\\tYear = {2004}}\\n\\n\",\"author_short\":[\"Marsh, W.\",\"Bearfield, G.\"],\"key\":\"marsh2004using\",\"id\":\"marsh2004using\",\"bibbaseid\":\"marsh-bearfield-usingbayesiannetworkstomodelaccidentcausationintheukrailwayindustry-2004\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\"],\"suffixes\":[]}],\"booktitle\":\"SAFECOMP'07 26th international conference on Computer Safety, Reliability, and Security\",\"date-modified\":\"2016-11-06 20:06:31 +0000\",\"doi\":\"10.1007/978-3-540-75101-4\",\"organization\":\"Springer Berlin Heidelberg\",\"pages\":\"120–133\",\"title\":\"Representing parameterised fault trees using Bayesian networks\",\"year\":\"2007\",\"bdsk-url-1\":\"http://dx.doi.org/10.1007/978-3-540-75101-4\",\"bibtex\":\"@inproceedings{marsh2007representing,\\n\\tAuthor = {Marsh, William and Bearfield, George},\\n\\tBooktitle = {SAFECOMP'07 26th international conference on Computer Safety, Reliability, and Security},\\n\\tDate-Modified = {2016-11-06 20:06:31 +0000},\\n\\tDoi = {10.1007/978-3-540-75101-4},\\n\\tOrganization = {Springer Berlin Heidelberg},\\n\\tPages = {120--133},\\n\\tTitle = {Representing parameterised fault trees using Bayesian networks},\\n\\tYear = {2007},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-540-75101-4}}\\n\\n\",\"author_short\":[\"Marsh, W.\",\"Bearfield, G.\"],\"key\":\"marsh2007representing\",\"id\":\"marsh2007representing\",\"bibbaseid\":\"marsh-bearfield-representingparameterisedfaulttreesusingbayesiannetworks-2007\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"abstract\":\"The Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"booktitle\":\"Ale, B, Papazuglo, I & Zio, E (eds) Back to the future. Proceedings of the European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, 5-9 September 2010. ISBN 0415604273\",\"date-modified\":\"2016-11-05 17:57:28 +0000\",\"publisher\":\"CRC Press Inc\",\"title\":\"Causal Modelling of Lower Consequence Rail Safety Incidents\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2\",\"year\":\"2010\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2\",\"bibtex\":\"@inproceedings{bearfield2010causal,\\n\\tAbstract = {The Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk.},\\n\\tAuthor = {Bearfield, George and Marsh, William},\\n\\tBooktitle = {Ale, B, Papazuglo, I \\\\& Zio, E (eds) Back to the future. Proceedings of the European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, 5-9 September 2010. ISBN 0415604273},\\n\\tDate-Modified = {2016-11-05 17:57:28 +0000},\\n\\tPublisher = {CRC Press Inc},\\n\\tTitle = {Causal Modelling of Lower Consequence Rail Safety Incidents},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2},\\n\\tYear = {2010},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2}}\\n\\n\",\"author_short\":[\"Bearfield, G.\",\"Marsh, W.\"],\"key\":\"bearfield2010causal\",\"id\":\"bearfield2010causal\",\"bibbaseid\":\"bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neil\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Cates\"],\"firstnames\":[\"Patrick\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Forey\"],\"firstnames\":[\"Simon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tailor\"],\"firstnames\":[\"Manesh\"],\"suffixes\":[]}],\"booktitle\":\"Software Engineering, 2004. 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20:31:20 +0000\",\"doi\":\"10.1016/j.infsof.2006.09.001\",\"journal\":\"Information and Software Technology\",\"number\":\"1\",\"pages\":\"32–43\",\"publisher\":\"Elsevier\",\"title\":\"Predicting software defects in varying development lifecycles using Bayesian nets\",\"url\":\"https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf\",\"volume\":\"49\",\"year\":\"2006\",\"bdsk-url-1\":\"https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.infsof.2006.09.001\",\"bibtex\":\"@article{fenton2006predicting,\\n\\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Marquez, David and Krause, Paul and Mishra, Rajat},\\n\\tDate-Modified = {2016-11-06 20:31:20 +0000},\\n\\tDoi = {10.1016/j.infsof.2006.09.001},\\n\\tJournal = {Information and Software Technology},\\n\\tNumber = {1},\\n\\tPages = {32--43},\\n\\tPublisher = {Elsevier},\\n\\tTitle = {Predicting software defects in varying development lifecycles using Bayesian nets},\\n\\tUrl = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},\\n\\tVolume = {49},\\n\\tYear = {2006},\\n\\tBdsk-Url-1 = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.infsof.2006.09.001}}\\n\\n\",\"author_short\":[\"Fenton, N.\",\"Neil, M.\",\"Marsh, W.\",\"Hearty, P.\",\"Marquez, D.\",\"Krause, P.\",\"Mishra, R.\"],\"key\":\"fenton2006predicting\",\"id\":\"fenton2006predicting\",\"bibbaseid\":\"fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neil\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hearty\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Radliński\"],\"firstnames\":[\"Łukasz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Krause\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-06 19:58:08 +0000\",\"doi\":\"10.1007/s10664-008-9072-x\",\"journal\":\"Empirical Software Engineering\",\"number\":\"5\",\"pages\":\"499–537\",\"publisher\":\"Springer US\",\"title\":\"On the effectiveness of early life cycle defect prediction with Bayesian Nets\",\"volume\":\"13\",\"year\":\"2008\",\"bdsk-url-1\":\"http://dx.doi.org/10.1007/s10664-008-9072-x\",\"bibtex\":\"@article{fenton2008effectiveness,\\n\\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radli{\\\\'n}ski, {\\\\L}ukasz and Krause, Paul},\\n\\tDate-Modified = {2016-11-06 19:58:08 +0000},\\n\\tDoi = {10.1007/s10664-008-9072-x},\\n\\tJournal = {Empirical Software Engineering},\\n\\tNumber = {5},\\n\\tPages = {499--537},\\n\\tPublisher = {Springer US},\\n\\tTitle = {On the effectiveness of early life cycle defect prediction with Bayesian Nets},\\n\\tVolume = {13},\\n\\tYear = {2008},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1007/s10664-008-9072-x}}\\n\\n\",\"author_short\":[\"Fenton, N.\",\"Neil, M.\",\"Marsh, W.\",\"Hearty, P.\",\"Radliński, Ł.\",\"Krause, P.\"],\"key\":\"fenton2008effectiveness\",\"id\":\"fenton2008effectiveness\",\"bibbaseid\":\"fenton-neil-marsh-hearty-radliski-krause-ontheeffectivenessofearlylifecycledefectpredictionwithbayesiannets-2008\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neil\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Hearty\"],\"firstnames\":[\"Peter\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Radlinski\"],\"firstnames\":[\"Lukasz\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Krause\"],\"firstnames\":[\"Paul\"],\"suffixes\":[]}],\"booktitle\":\"Proceeding PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering\",\"date-modified\":\"2016-11-06 20:24:32 +0000\",\"doi\":\"10.1109/PROMISE.2007.11\",\"keywords\":\"Software projects\",\"organization\":\"IEEE\",\"pages\":\"2–11\",\"title\":\"Project data incorporating qualitative factors for improved software defect prediction\",\"year\":\"2007\",\"bdsk-url-1\":\"http://dx.doi.org/10.1109/PROMISE.2007.11\",\"bibtex\":\"@inproceedings{fenton2007project,\\n\\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radlinski, Lukasz and Krause, Paul},\\n\\tBooktitle = {Proceeding PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering},\\n\\tDate-Modified = {2016-11-06 20:24:32 +0000},\\n\\tDoi = {10.1109/PROMISE.2007.11},\\n\\tKeywords = {Software projects},\\n\\tOrganization = {IEEE},\\n\\tPages = {2--11},\\n\\tTitle = {Project data incorporating qualitative factors for improved software defect prediction},\\n\\tYear = {2007},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1109/PROMISE.2007.11}}\\n\\n\",\"author_short\":[\"Fenton, N.\",\"Neil, M.\",\"Marsh, W.\",\"Hearty, P.\",\"Radlinski, L.\",\"Krause, P.\"],\"key\":\"fenton2007project\",\"id\":\"fenton2007project\",\"bibbaseid\":\"fenton-neil-marsh-hearty-radlinski-krause-projectdataincorporatingqualitativefactorsforimprovedsoftwaredefectprediction-2007\",\"role\":\"author\",\"urls\":{},\"keyword\":[\"Software projects\"],\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bastani\"],\"firstnames\":[\"Kaveh\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Raharjo\"],\"firstnames\":[\"Hendry\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Lifvergren\"],\"firstnames\":[\"Svante\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bergman\"],\"firstnames\":[\"Bo\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:36:58 +0000\",\"doi\":\"10.1016/j.dss.2012.10.007\",\"journal\":\"Decision Support Systems\",\"number\":\"2\",\"pages\":\"488–498\",\"publisher\":\"North-Holland\",\"title\":\"Decision support system for Warfarin therapy management using Bayesian networks\",\"volume\":\"55\",\"year\":\"2013\",\"bdsk-url-1\":\"http://dx.doi.org/10.1016/j.dss.2012.10.007\",\"bibtex\":\"@article{yet2013decision,\\n\\tAuthor = {Yet, Barbaros and Bastani, Kaveh and Raharjo, Hendry and Lifvergren, Svante and Marsh, William and Bergman, Bo},\\n\\tDate-Modified = {2016-11-05 23:36:58 +0000},\\n\\tDoi = {10.1016/j.dss.2012.10.007},\\n\\tJournal = {Decision Support Systems},\\n\\tNumber = {2},\\n\\tPages = {488--498},\\n\\tPublisher = {North-Holland},\\n\\tTitle = {Decision support system for Warfarin therapy management using Bayesian networks},\\n\\tVolume = {55},\\n\\tYear = {2013},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.dss.2012.10.007}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Bastani, K.\",\"Raharjo, H.\",\"Lifvergren, S.\",\"Marsh, W.\",\"Bergman, B.\"],\"key\":\"yet2013decision\",\"id\":\"yet2013decision\",\"bibbaseid\":\"yet-bastani-raharjo-lifvergren-marsh-bergman-decisionsupportsystemforwarfarintherapymanagementusingbayesiannetworks-2013\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model confiorms to the clinician's way of reasoning and so that we can predict the probable effect of the available interventions. Since we cannot always depend on data from controlled trials, we depend instead on 'clinical knowledge' and it is therefore vital that this elicited rigorously. We propose three stages of knowledge modeling covering the treatment process, the information generated by the process and the causal relationship. These stages lead to a causal Bayesian network, which is used to predict the patient outcome under different treatment options.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Zane\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 17:51:59 +0000\",\"journal\":\"Probabilistic Problem Solving in BioMedicine\",\"pages\":\"107–120\",\"title\":\"Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2\",\"year\":\"2011\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2\",\"bibtex\":\"@article{yet2011towards,\\n\\tAbstract = {We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model confiorms to the clinician's way of reasoning and so that we can predict the probable effect of the available interventions. Since we cannot always depend on data from controlled trials, we depend instead on 'clinical knowledge' and it is therefore vital that this elicited rigorously. We propose three stages of knowledge modeling covering the treatment process, the information generated by the process and the causal relationship. These stages lead to a causal Bayesian network, which is used to predict the patient outcome under different treatment options.},\\n\\tAuthor = {Yet, Barbaros and Perkins, Zane and Marsh, William and Fenton, Norman},\\n\\tDate-Modified = {2016-11-05 17:51:59 +0000},\\n\\tJournal = {Probabilistic Problem Solving in BioMedicine},\\n\\tPages = {107--120},\\n\\tTitle = {Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2},\\n\\tYear = {2011},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Perkins, Z.\",\"Marsh, W.\",\"Fenton, N.\"],\"key\":\"yet2011towards\",\"id\":\"yet2011towards\",\"bibbaseid\":\"yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Holloway\"],\"firstnames\":[\"Anna\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"George\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:37:33 +0000\",\"doi\":\"10.1177/0954409713498381\",\"journal\":\"Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit\",\"number\":\"6\",\"pages\":\"704–714\",\"publisher\":\"Sage Publications\",\"title\":\"Change and safety: decision-making from data\",\"volume\":\"227\",\"year\":\"2013\",\"bdsk-url-1\":\"http://dx.doi.org/10.1177/0954409713498381\",\"bibtex\":\"@article{holloway2013change,\\n\\tAuthor = {Holloway, Anna and Marsh, William and Bearfield, George},\\n\\tDate-Modified = {2016-11-05 23:37:33 +0000},\\n\\tDoi = {10.1177/0954409713498381},\\n\\tJournal = {Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit},\\n\\tNumber = {6},\\n\\tPages = {704--714},\\n\\tPublisher = {Sage Publications},\\n\\tTitle = {Change and safety: decision-making from data},\\n\\tVolume = {227},\\n\\tYear = {2013},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1177/0954409713498381}}\\n\\n\",\"author_short\":[\"Holloway, A.\",\"Marsh, W.\",\"Bearfield, G.\"],\"key\":\"holloway2013change\",\"id\":\"holloway2013change\",\"bibbaseid\":\"holloway-marsh-bearfield-changeandsafetydecisionmakingfromdata-2013\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Carre\"],\"firstnames\":[\"Bernard\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Garnsworthy\"],\"firstnames\":[\"Jon\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"booktitle\":\"Ada in transition: Proceedings of the 1992 Ada UK International Conference\",\"date-modified\":\"2016-11-06 22:36:19 +0000\",\"editor\":[{\"propositions\":[],\"lastnames\":[\"Taylor\"],\"firstnames\":[\"W\",\"J\"],\"suffixes\":[]}],\"month\":\"Oct\",\"number\":\"4\",\"pages\":\"31–45\",\"publisher\":\"IOS Press\",\"series\":\"Studies in computer and communications systems\",\"title\":\"SPARK—a safetyrelated Ada subset\",\"url\":\"https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK—a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false\",\"year\":\"1992\",\"bdsk-url-1\":\"https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK—a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false\",\"bibtex\":\"@inproceedings{carre1992spark,\\n\\tAuthor = {Carre, Bernard and Garnsworthy, Jon and Marsh, William},\\n\\tBooktitle = {Ada in transition: Proceedings of the 1992 Ada UK International Conference},\\n\\tDate-Modified = {2016-11-06 22:36:19 +0000},\\n\\tEditor = {Taylor, W J},\\n\\tMonth = {Oct},\\n\\tNumber = {4},\\n\\tPages = {31--45},\\n\\tPublisher = {IOS Press},\\n\\tSeries = {Studies in computer and communications systems},\\n\\tTitle = {SPARK---a safetyrelated Ada subset},\\n\\tUrl = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK---a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false},\\n\\tYear = {1992},\\n\\tBdsk-Url-1 = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK---a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false}}\\n\\n\",\"author_short\":[\"Carre, B.\",\"Garnsworthy, J.\",\"Marsh, W.\"],\"editor_short\":[\"Taylor, W J\"],\"key\":\"carre1992spark\",\"id\":\"carre1992spark\",\"bibbaseid\":\"carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK—a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"W\"],\"suffixes\":[]}],\"journal\":\"Microprocessors and Microsystems\",\"number\":\"1\",\"pages\":\"41–47\",\"publisher\":\"Elsevier\",\"title\":\"Harmonisation of defence standards for safety-critical software\",\"volume\":\"21\",\"year\":\"1997\",\"bibtex\":\"@article{marsh1997harmonisation,\\n\\tAuthor = {Marsh, W},\\n\\tJournal = {Microprocessors and Microsystems},\\n\\tNumber = {1},\\n\\tPages = {41--47},\\n\\tPublisher = {Elsevier},\\n\\tTitle = {Harmonisation of defence standards for safety-critical software},\\n\\tVolume = {21},\\n\\tYear = {1997}}\\n\\n\",\"author_short\":[\"Marsh, W\"],\"key\":\"marsh1997harmonisation\",\"id\":\"marsh1997harmonisation\",\"bibbaseid\":\"marsh-harmonisationofdefencestandardsforsafetycriticalsoftware-1997\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Zane\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tai\"],\"firstnames\":[\"Nigel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:33:44 +0000\",\"doi\":\"10.1016/j.jbi.2013.10.012\",\"journal\":\"Journal of biomedical informatics\",\"pages\":\"28–37\",\"publisher\":\"Academic Press\",\"title\":\"Not just data: A method for improving prediction with knowledge\",\"volume\":\"48\",\"year\":\"2014\",\"bdsk-url-1\":\"http://dx.doi.org/10.1016/j.jbi.2013.10.012\",\"bibtex\":\"@article{yet2014not,\\n\\tAbstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},\\n\\tAuthor = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},\\n\\tDate-Modified = {2016-11-05 23:33:44 +0000},\\n\\tDoi = {10.1016/j.jbi.2013.10.012},\\n\\tJournal = {Journal of biomedical informatics},\\n\\tPages = {28--37},\\n\\tPublisher = {Academic Press},\\n\\tTitle = {Not just data: A method for improving prediction with knowledge},\\n\\tVolume = {48},\\n\\tYear = {2014},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2013.10.012}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Perkins, Z.\",\"Fenton, N.\",\"Tai, N.\",\"Marsh, W.\"],\"key\":\"yet2014not\",\"id\":\"yet2014not\",\"bibbaseid\":\"yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four `abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D\",\"William\",\"R\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:34:05 +0000\",\"doi\":\"10.1016/j.knosys.2014.02.020\",\"journal\":\"Knowledge-Based Systems\",\"pages\":\"84–97\",\"publisher\":\"Elsevier\",\"title\":\"Compatible and incompatible abstractions in Bayesian networks\",\"volume\":\"62\",\"year\":\"2014\",\"bdsk-url-1\":\"http://dx.doi.org/10.1016/j.knosys.2014.02.020\",\"bibtex\":\"@article{yet2014compatible,\\n\\tAbstract = {The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four `abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.},\\n\\tAuthor = {Yet, Barbaros and Marsh, D William R},\\n\\tDate-Modified = {2016-11-05 23:34:05 +0000},\\n\\tDoi = {10.1016/j.knosys.2014.02.020},\\n\\tJournal = {Knowledge-Based Systems},\\n\\tPages = {84--97},\\n\\tPublisher = {Elsevier},\\n\\tTitle = {Compatible and incompatible abstractions in Bayesian networks},\\n\\tVolume = {62},\\n\\tYear = {2014},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.knosys.2014.02.020}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Marsh, D W. R\"],\"key\":\"yet2014compatible\",\"id\":\"yet2014compatible\",\"bibbaseid\":\"yet-marsh-compatibleandincompatibleabstractionsinbayesiannetworks-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report `univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model – ignoring some complexities of the problem – or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Zane\",\"B\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Rasmussen\"],\"firstnames\":[\"Todd\",\"E\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tai\"],\"firstnames\":[\"Nigel\",\"RM\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"D\",\"William\",\"R\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:34:14 +0000\",\"doi\":\"10.1016/j.jbi.2014.07.018\",\"journal\":\"Journal of biomedical informatics\",\"pages\":\"373–385\",\"publisher\":\"Academic Press\",\"title\":\"Combining data and meta-analysis to build Bayesian networks for clinical decision support\",\"volume\":\"52\",\"year\":\"2014\",\"bdsk-url-1\":\"http://dx.doi.org/10.1016/j.jbi.2014.07.018\",\"bibtex\":\"@article{yet2014combining,\\n\\tAbstract = {Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report `univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model -- ignoring some complexities of the problem -- or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.},\\n\\tAuthor = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel RM and Marsh, D William R},\\n\\tDate-Modified = {2016-11-05 23:34:14 +0000},\\n\\tDoi = {10.1016/j.jbi.2014.07.018},\\n\\tJournal = {Journal of biomedical informatics},\\n\\tPages = {373--385},\\n\\tPublisher = {Academic Press},\\n\\tTitle = {Combining data and meta-analysis to build Bayesian networks for clinical decision support},\\n\\tVolume = {52},\\n\\tYear = {2014},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2014.07.018}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Perkins, Z. B\",\"Rasmussen, T. E\",\"Tai, N. R.\",\"Marsh, D W. R\"],\"key\":\"yet2014combining\",\"id\":\"yet2014combining\",\"bibbaseid\":\"yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management – Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Constantinou\"],\"firstnames\":[\"Anthony\",\"Costa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Freestone\"],\"firstnames\":[\"Mark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Coid\"],\"firstnames\":[\"Jeremy\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:33:14 +0000\",\"doi\":\"10.1016/j.eswa.2015.05.025\",\"journal\":\"Expert Systems with Applications\",\"number\":\"21\",\"pages\":\"7511–7529\",\"publisher\":\"Pergamon\",\"title\":\"Risk assessment and risk management of violent reoffending among prisoners\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2\",\"volume\":\"42\",\"year\":\"2015\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.eswa.2015.05.025\",\"bibtex\":\"@article{constantinou2015risk,\\n\\tAbstract = {Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management -- Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.},\\n\\tAuthor = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Fenton, Norman and Coid, Jeremy},\\n\\tDate-Modified = {2016-11-05 23:33:14 +0000},\\n\\tDoi = {10.1016/j.eswa.2015.05.025},\\n\\tJournal = {Expert Systems with Applications},\\n\\tNumber = {21},\\n\\tPages = {7511--7529},\\n\\tPublisher = {Pergamon},\\n\\tTitle = {Risk assessment and risk management of violent reoffending among prisoners},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},\\n\\tVolume = {42},\\n\\tYear = {2015},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.eswa.2015.05.025}}\\n\\n\",\"author_short\":[\"Constantinou, A. C.\",\"Freestone, M.\",\"Marsh, W.\",\"Fenton, N.\",\"Coid, J.\"],\"key\":\"constantinou2015risk\",\"id\":\"constantinou2015risk\",\"bibbaseid\":\"constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"DWR\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Bearfield\"],\"firstnames\":[\"GJ\"],\"suffixes\":[]}],\"date-modified\":\"2016-02-20 17:16:42 +0000\",\"journal\":\"Proceedings of ESREL 2007, Stavanger, Norway\",\"month\":\"June\",\"title\":\"Merging Event Trees using Bayesian Networks\",\"year\":\"2007\",\"bibtex\":\"@article{marsh2007merging,\\n\\tAuthor = {Marsh, DWR and Bearfield, GJ},\\n\\tDate-Modified = {2016-02-20 17:16:42 +0000},\\n\\tJournal = {Proceedings of ESREL 2007, Stavanger, Norway},\\n\\tMonth = {June},\\n\\tTitle = {Merging Event Trees using Bayesian Networks},\\n\\tYear = {2007}}\\n\\n\",\"author_short\":[\"Marsh, D.\",\"Bearfield, G.\"],\"key\":\"marsh2007merging\",\"id\":\"marsh2007merging\",\"bibbaseid\":\"marsh-bearfield-mergingeventtreesusingbayesiannetworks-2007\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Constantinou\"],\"firstnames\":[\"Anthony\",\"Costa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Freestone\"],\"firstnames\":[\"Mark\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Coid\"],\"firstnames\":[\"Jeremy\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:33:35 +0000\",\"doi\":\"10.1016/j.dss.2015.09.006\",\"journal\":\"Decision Support Systems\",\"pages\":\"42–55\",\"publisher\":\"North-Holland\",\"title\":\"Causal inference for violence risk management and decision support in forensic psychiatry\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774\",\"volume\":\"80\",\"year\":\"2015\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.dss.2015.09.006\",\"bibtex\":\"@article{constantinou2015causal,\\n\\tAbstract = {The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.},\\n\\tAuthor = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Coid, Jeremy},\\n\\tDate-Modified = {2016-11-05 23:33:35 +0000},\\n\\tDoi = {10.1016/j.dss.2015.09.006},\\n\\tJournal = {Decision Support Systems},\\n\\tPages = {42--55},\\n\\tPublisher = {North-Holland},\\n\\tTitle = {Causal inference for violence risk management and decision support in forensic psychiatry},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},\\n\\tVolume = {80},\\n\\tYear = {2015},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.dss.2015.09.006}}\\n\\n\",\"author_short\":[\"Constantinou, A. C.\",\"Freestone, M.\",\"Marsh, W.\",\"Coid, J.\"],\"key\":\"constantinou2015causal\",\"id\":\"constantinou2015causal\",\"bibbaseid\":\"constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Objectives Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. Method The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. Results The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). Conclusions We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Constantinou\"],\"firstnames\":[\"Anthony\",\"Costa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Neil\"],\"firstnames\":[\"Martin\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:30:54 +0000\",\"doi\":\"10.1016/j.artmed.2015.09.002\",\"journal\":\"Artificial Intelligence in Medicine\",\"pages\":\"41–52\",\"title\":\"Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2\",\"volume\":\"66\",\"year\":\"2016\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.artmed.2015.09.002\",\"bibtex\":\"@article{constantinou2015value,\\n\\tAbstract = {Objectives\\nInspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.\\n\\nMethod\\nThe method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks.\\n\\nResults\\nThe method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%).\\n\\nConclusions\\nWe have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.},\\n\\tAuthor = {Constantinou, Anthony Costa and Yet, Barbaros and Fenton, Norman and Neil, Martin and Marsh, William},\\n\\tDate-Modified = {2016-11-05 23:30:54 +0000},\\n\\tDoi = {10.1016/j.artmed.2015.09.002},\\n\\tJournal = {Artificial Intelligence in Medicine},\\n\\tPages = {41--52},\\n\\tTitle = {Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},\\n\\tVolume = {66},\\n\\tYear = {2016},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2015.09.002}}\\n\\n\",\"author_short\":[\"Constantinou, A. C.\",\"Yet, B.\",\"Fenton, N.\",\"Neil, M.\",\"Marsh, W.\"],\"key\":\"constantinou2015value\",\"id\":\"constantinou2015value\",\"bibbaseid\":\"constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2\"},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"inproceedings\",\"type\":\"inproceedings\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Winther\"],\"firstnames\":[\"Rune\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"booktitle\":\"ESREL 2013. Safety, Reliability and Risk Analysis: Beyond the Horizon\",\"date-modified\":\"2016-11-05 23:36:02 +0000\",\"doi\":\"10.1201/b15938-381\",\"organization\":\"CRC Press\",\"pages\":\"2545–2553\",\"title\":\"Hazards, accidents and events—a land of confusing terms\",\"year\":\"2013\",\"bdsk-url-1\":\"http://dx.doi.org/10.1201/b15938-381\",\"bibtex\":\"@inproceedings{winther2013hazards,\\n\\tAuthor = {Winther, Rune and Marsh, William},\\n\\tBooktitle = {ESREL 2013. Safety, Reliability and Risk Analysis: Beyond the Horizon},\\n\\tDate-Modified = {2016-11-05 23:36:02 +0000},\\n\\tDoi = {10.1201/b15938-381},\\n\\tOrganization = {CRC Press},\\n\\tPages = {2545--2553},\\n\\tTitle = {Hazards, accidents and events---a land of confusing terms},\\n\\tYear = {2013},\\n\\tBdsk-Url-1 = {http://dx.doi.org/10.1201/b15938-381}}\\n\\n\",\"author_short\":[\"Winther, R.\",\"Marsh, W.\"],\"key\":\"winther2013hazards\",\"id\":\"winther2013hazards\",\"bibbaseid\":\"winther-marsh-hazardsaccidentsandeventsalandofconfusingterms-2013\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":1,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Yet\"],\"firstnames\":[\"Barbaros\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Perkins\"],\"firstnames\":[\"Zane\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Tai\"],\"firstnames\":[\"Nigel\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 17:42:22 +0000\",\"doi\":\"10.3233/978-1-61499-432-9-53\",\"journal\":\"Studies in Health Technology and Informatics\",\"pages\":\"53–7\",\"title\":\"Explicit evidence for prognostic Bayesian network models\",\"volume\":\"205\",\"year\":\"2014\",\"bdsk-file-1\":\"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\",\"bdsk-url-1\":\"https://doi.org/10.3233/978-1-61499-432-9-53\",\"bibtex\":\"@article{yet2014explicit,\\n\\tAbstract = {Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.},\\n\\tAuthor = {Yet, Barbaros and Perkins, Zane and Tai, Nigel and Marsh, William},\\n\\tDate-Modified = {2016-11-05 17:42:22 +0000},\\n\\tDoi = {10.3233/978-1-61499-432-9-53},\\n\\tJournal = {Studies in Health Technology and Informatics},\\n\\tPages = {53--7},\\n\\tTitle = {Explicit evidence for prognostic Bayesian network models},\\n\\tVolume = {205},\\n\\tYear = {2014},\\n\\tBdsk-File-1 = {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},\\n\\tBdsk-Url-1 = {https://doi.org/10.3233/978-1-61499-432-9-53}}\\n\\n\",\"author_short\":[\"Yet, B.\",\"Perkins, Z.\",\"Tai, N.\",\"Marsh, W.\"],\"key\":\"yet2014explicit\",\"id\":\"yet2014explicit\",\"bibbaseid\":\"yet-perkins-tai-marsh-explicitevidenceforprognosticbayesiannetworkmodels-2014\",\"role\":\"author\",\"urls\":{},\"metadata\":{\"authorlinks\":{}},\"downloads\":0,\"html\":\"\"},{\"bibtype\":\"article\",\"type\":\"article\",\"abstract\":\"OBJECTIVES: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.\",\"author\":[{\"propositions\":[],\"lastnames\":[\"Constantinou\"],\"firstnames\":[\"Anthony\",\"Costa\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Fenton\"],\"firstnames\":[\"Norman\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Marsh\"],\"firstnames\":[\"William\"],\"suffixes\":[]},{\"propositions\":[],\"lastnames\":[\"Radlinski\"],\"firstnames\":[\"Lukasz\"],\"suffixes\":[]}],\"date-modified\":\"2016-11-05 23:32:53 +0000\",\"doi\":\"10.1016/j.artmed.2016.01.002\",\"journal\":\"Artificial Intelligence in Medicine\",\"pages\":\"75-93\",\"title\":\"From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support\",\"url\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587\",\"volume\":\"67\",\"year\":\"2016\",\"bdsk-url-1\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587\",\"bdsk-url-2\":\"http://dx.doi.org/10.1016/j.artmed.2016.01.002\",\"bibtex\":\"@article{constantinou2016complex,\\n\\tAbstract = {OBJECTIVES: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.},\\n\\tAuthor = {Constantinou, Anthony Costa and Fenton, Norman and Marsh, William and Radlinski, Lukasz},\\n\\tDate-Modified = {2016-11-05 23:32:53 +0000},\\n\\tDoi = {10.1016/j.artmed.2016.01.002},\\n\\tJournal = {Artificial Intelligence in Medicine},\\n\\tPages = {75-93},\\n\\tTitle = {From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support},\\n\\tUrl = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},\\n\\tVolume = {67},\\n\\tYear = {2016},\\n\\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},\\n\\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2016.01.002}}\\n\",\"author_short\":[\"Constantinou, A. C.\",\"Fenton, N.\",\"Marsh, W.\",\"Radlinski, L.\"],\"key\":\"constantinou2016complex\",\"id\":\"constantinou2016complex\",\"bibbaseid\":\"constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016\",\"role\":\"author\",\"urls\":{\"Paper\":\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587\"},\"metadata\":{\"authorlinks\":{}},\"html\":\"\"}],\n      metadata: {\"authorlinks\":{}},\n      ownerID: undefined\n    };\n  </script>\n\n  <script type=\"text/javascript\">\n  var site$;\n  if (typeof $ != 'undefined') {\n    console.log('existing jquery: storing and then restoring');\n    site$ = $;\n    $ = null;\n  }\n  </script>\n\n  <script src=\"https://ajax.googleapis.com/ajax/libs/jquery/2.2.4/jquery.min.js\">\n  </script>\n  <script type=\"text/javascript\">\n  if (site$) {\n    console.log('existing jquery: avoiding conflict and restoring');\n    bb$ = $.noConflict(true);\n    $ = site$;\n  } else {\n    bb$ = $;\n  }\n  </script>\n\n  <script src=\"//bibbase.org/js/bibbase.min.js\"\n          type=\"text/javascript\">\n  </script>\n\n  <script type=\"text/javascript\"\n          src=\"https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS-MML_HTMLorMML\">\n  </script>\n  <script type=\"text/x-mathjax-config\">\n    MathJax.Hub.Config({tex2jax: {inlineMath: [['$','$'], ['\\\\(','\\\\)']]},\n                        skipTags: [\"body\"],\n                        processClass: \"bibbase_paper\"});\n  </script>\n\n  <style>\n  @media print {\n    .dontprint {\n        display: none !important;\n    }\n\n  .bibbase_group {\n    background-color: #E0E0E0;\n    font-style: italic;\n    font-size: larger;\n    text-shadow: 0px 0px 3px #FFFFFF;\n    border-radius: 4px 4px 4px 4px;\n    -moz-border-radius: 4px 4px 4px 4px;\n    -webkit-border-radius: 4px;\n    padding: 5px 40px 5px;\n    margin-top: 10px;\n    margin-bottom: 5px;\n    cursor: pointer;\n  }\n\n  .bibbase_paper {\n    margin-bottom: 5px;\n  }\n\n  }\n  </style>\n\n  \n  <div style=\"float: right; margin-right: 10px; margin-top: 10px; text-align: right; font-size: 12px\">\n    generated by\n    <a href=\"//bibbase.org\"\n       onclick=\"trackOutboundLink('bibbase', 'logo clicked', window.location.href, '//bibbase.org'); return false;\">\n      <img src=\"//bibbase.org/img/logo.svg\"\n           style=\"vertical-align: middle; margin-left: 3px; height: 16px;\"/\n           alt=\"bibbase.org\"\n           title=\"BibBase – The easiest way to maintain your publications page.\"\n        ></a>\n    \n  </div>\n  \n\n  <div id=\"bibbase_header\" class=\"dontprint\" style=\"\">\n\n    <ul class=\"nav nav-pills\" style=\"margin: 0px;\">\n\n      <li class=\"dropdown\" id=\"groupby_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\">\n          Group by\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li class=\"groupby year\"><a onclick=\"groupby('year')\">\n            Year</a>\n        </li>\n        <li class=\"groupby author\"><a onclick=\"groupby('author_short')\">\n            Author</a>\n        </li>\n        <li class=\"groupby type\"><a onclick=\"groupby('type')\">\n            Type</a>\n        </li>\n        <li class=\"groupby keyword\"><a onclick=\"groupby('keyword')\">\n            Keyword</a>\n        </li>\n        <li class=\"groupby downloads\"><a onclick=\"groupby('downloads')\">\n            Downloads</a>\n        </li>\n      </ul>\n      </li>\n\n\n      <li class=\"dropdown\" id=\"menu_dropdown\">\n      <a class=\"dropdown-toggle\"\n         data-toggle=\"dropdown\"\n         href=\"#\" alt=\"controls\">\n          <i class=\"fa fa-reorder\" alt=\"controls\"></i>\n          <b class=\"caret\"></b>\n      </a>\n      <ul class=\"dropdown-menu\">\n        <li>\n          <a onclick=\"toggleAll()\">\n            <i class=\"fa fa-chevron-down fa-fw\"></i> Expand/Collapse All\n          </a>\n        </li>\n        <li>\n          <a download=\"citations-ref.bib\" href=\"data:text/bibtex;base64,@article{Yet2017,
	Abstract = {There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.},
	Author = {Yet, Barbaros and Perkins, Zane B. and Tai, Nigel R. M. and Marsh, D. William R.},
	Date-Added = {2019-03-09 15:35:24 +0000},
	Date-Modified = {2019-03-09 15:35:24 +0000},
	Doi = {10.1007/s10115-016-0932-1},
	Issn = {0219-3116},
	Journal = {Knowledge and Information Systems},
	Month = {jan},
	Pages = {1--27},
	Title = {Clinical evidence framework for Bayesian networks},
	Year = {2017},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/s10115-016-0932-1}}



@inproceedings{Marsh:2017aa,
	Abstract = {To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.},
	Author = {Marsh, D. W. R. and Zhang, H.},
	Booktitle = {Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)},
	Date-Added = {2019-03-09 15:34:48 +0000},
	Date-Modified = {2019-03-09 15:34:48 +0000},
	Editor = {Lesley Walls and Matthew Revie and Tim Bedford},
	Month = {Sep},
	Pages = {1056-1063},
	Publisher = {CRC Press Reference - 486 Pages CRC Press ISBN 9781138029972},
	Title = {{Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgement}},
	Year = {2017}}



@misc{waite2017k5design,
	Abstract = {Recent research with middle school and university students highlights two factors that contribute to programming success: 1) understanding the level of abstraction that you are working at, and 2) being able to move between levels. In this qualitative study, we explored levels of abstraction, and particularly the design level,with five K-5 teachers. Here we outline 11 main findings. The teachers interviewed use the design level for both programming and writing. However, the two expert computing teachers have a far greater depth of understanding of the opportunities for the use of the design level, supporting pupils to understand the level they are
working at and helping them move between levels of abstraction by using designs in novel ways. Further work is needed to investigate whether our results are generalisable. Further exploration of levels of abstraction and particularly how the design level helps K-5 learners learn to program, in the same way, that planning supports novices learning to write, is warranted.},
	Author = {WAITE, JL and curzon, P and marsh, D and Sentance, S},
	Conference = {WiPSCE 2017},
	Date-Added = {2019-03-09 15:34:42 +0000},
	Date-Modified = {2019-03-09 15:34:42 +0000},
	Day = {9},
	Keyword = {Design},
	Month = {Nov},
	Title = {K-5 Teachers' Uses of Levels of Abstraction Focusing on Design},
	Year = {2017}}



@article{yet2018towardsconditions,
	Abstract = {Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.},
	Author = {Yet, B and Marsh, W and Morrissey, D},
	Date-Added = {2019-03-09 15:34:38 +0000},
	Date-Modified = {2019-03-09 15:34:38 +0000},
	Eissn = {1879-8365},
	Journal = {Stud Health Technol Inform},
	Keyword = {musculoskeletal},
	Language = {eng},
	Organization = {Netherlands},
	Pages = {175--179},
	Publicationstatus = {published},
	Title = {Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions.},
	Url = {https://www.ncbi.nlm.nih.gov/pubmed/30306931},
	Volume = {255},
	Year = {2018},
	Bdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/30306931}}



@article{waite2018abstractionprogramming,
	Abstract = {Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess `do-ability'. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers'
knowledge of terms including algorithm and code; a concept of `emergent algorithms' is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study's results are generalisable more widely.},
	Address = {On line Journal},
	Author = {WAITE, JL and CURZON, P and MARSH, D and Sentance, S and Hawden-Bennett, A},
	Date-Added = {2019-03-09 15:34:34 +0000},
	Date-Modified = {2019-03-09 15:34:34 +0000},
	Day = {31},
	Doi = {10.21585/ijcses.v2i1.23},
	Editor = {Kalelioglu, F and Allsop, Y},
	Issn = {2513-8359},
	Issue = {1},
	Journal = {International Journal Of Computer Science Education In Schools},
	Keyword = {Computing Eductation},
	Month = {Jan},
	Number = {2},
	Pages = {14--40},
	Publicationstatus = {published},
	Title = {Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming},
	Url = {http://www.ijcses.org/index.php/ijcses/index},
	Volume = {2},
	Year = {2018},
	Bdsk-Url-1 = {http://www.ijcses.org/index.php/ijcses/index},
	Bdsk-Url-2 = {https://doi.org/10.21585/ijcses.v2i1.23}}



@article{mclachlan2018learningchallenge,
	Author = {McLachlan, S and Dube, K and Buchanan, D and Lean, S and Johnson, O and Potts, H and Gallagher, T and Marsh, W and Fenton, N},
	Date-Added = {2019-03-09 15:34:29 +0000},
	Date-Modified = {2019-03-09 15:34:29 +0000},
	Day = {27},
	Doi = {10.14236/jhi.v25i1.981},
	Eissn = {2058-4563},
	Issue = {1},
	Journal = {J Innov Health Inform},
	Keyword = {Learning Health Systems, Electronic Health Records},
	Language = {eng},
	Month = {Mar},
	Organization = {England},
	Pages = {981},
	Publicationstatus = {online-published},
	Title = {Learning Health Systems: The research community awareness challenge.},
	Url = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},
	Volume = {25},
	Year = {2018},
	Bdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},
	Bdsk-Url-2 = {https://doi.org/10.14236/jhi.v25i1.981}}



@inproceedings{zhang2018towardsmodels,
	Abstract = {Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.},
	Author = {ZHANG, H and MARSH, DWR},
	Booktitle = {ESREL2018 conference proceeding},
	Conference = {European Safety and Reliability Conference ESREL 2018},
	Date-Added = {2019-03-09 15:34:26 +0000},
	Date-Modified = {2019-03-09 15:34:26 +0000},
	Day = {17},
	Finishday = {21},
	Finishmonth = {Jun},
	Finishyear = {2018},
	Keyword = {probabilistic relational model},
	Month = {Jun},
	Organization = {Trondheim, Norway},
	Publicationstatus = {accepted},
	Publisher = {Taylor \& Francis},
	Startday = {17},
	Startmonth = {Jun},
	Startyear = {2018},
	Title = {Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models},
	Url = {http://haoyuan.uk/},
	Year = {2018},
	Bdsk-Url-1 = {http://haoyuan.uk/}}



@article{mclachlan2018thesystems,
	Abstract = {There are many proposed benefits of using learning health systems  (LHS), including improved patient outcomes. There has been little adoption of LHS  in  practice  due  to  challenges  and  barriers  that  limit  adoption  of  new  data-driven  technologies  in  healthcare.  We  have  identified  a  more  fundamental  explanation:  the  majority  of  developments  in  LHS  are  not  identified  as  LHS.  The  absence  of  a  unifying  namespace  and  framework  brings  a  lack  of  consistency  in  how  LHS  are identified and classified. As a result, the LHS `community' is fragmented, with  groups working on similar systems being not aware of each other's work. This leads  to duplication and the lack of a critical mass of researchers necessary to address  barriers to adoption.
Objective  To  find  a  way  to  support  easy  identification  and  classification  of  research works within the domain of LHS.
Method  A qualitative meta-narrative study focusing on works that self-identified  as LHS was used for two purposes. First, to find existing standard definitions and  frameworks  using  these  to  create  a  new  unifying  framework.  Second,  seeking  whether it was possible to classify those LHS solutions within the new framework. Results  The study found that with apparently limited awareness, all current LHS  works  fall  within  nine  primary  archetypes.  These  findings  were  used  to  develop a  unifying  framework  for  LHS  to  classify  works  as  LHS,  and  reduce  diversity  and  fragmentation within the domain.
Conclusion  Our finding brings clarification where there has been limited aware- ness  for  LHS  among  researchers.  We  believe  our  framework  is  simple  and  may  help researchers to classify works in the LHS domain. This framework may enable  realisation  of  the  critical  mass  necessary  to  bring  more  substantial  collaboration  and funding to LHS. Ongoing research will seek to establish the framework's effect  on the LHS domain.},
	Author = {MCLACHLAN, S and Potts, HWW and Dube, K and Buchanan, D and Lean, S and Gallagher, T and Johnson, O and DALEY, B and Marsh, W and FENTON, N},
	Date-Added = {2019-03-09 15:34:21 +0000},
	Date-Modified = {2019-03-09 15:34:21 +0000},
	Day = {15},
	Doi = {10.14236/jhi.v25i2.996},
	Issue = {2},
	Journal = {BCS Journal of Innovation in Health Informatics},
	Keyword = {precision medicine},
	Month = {Jun},
	Publicationstatus = {published},
	Title = {The Heimdall framework for supporting characterisation of learning health systems},
	Volume = {25},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.14236/jhi.v25i2.996}}



@inproceedings{waite2018comparingwriting,
	Abstract = {K-5 teachers teach a range of subjects \& develop generic teaching
skills; when starting to teach computing, particularly programming,
practitioners may not realise that they can draw on these other
skills to support their teaching. In a small study of K-5 teachers,
potential synergies were suggested between using planning in the
the teaching of writing and design in the teaching of programming.
In this paper, we explore these synergies by surveying a wider group
of teachers (n=207) on their uses of planning and design. Teachers
reported the usefulness of planning for writing and design for
programming as equally important. However, there were significant
differences in their uses. The majority saw planning as essential in
writing \& put this into practice in their teaching. For example, they
demonstrated the creation of plans, expected students to annotate
plans, required students to refer to plans when writing and used
plans to differentiate. By contrast, these uses were implemented less
frequently in programming tasks. We also report on differences in
the confidence of male \& female respondents, \& between generalists
(who teach programming \& writing) \& specialists (who do not
teach writing). For example, females were more confident to teach
writing than programming, with males vice versa. Having revealed
opportunities for reuse of successful techniques used in teaching
writing for the teaching of programming we recommend further
work is needed to explore this transfer of pedagogical knowledge.},
	Author = {WAITE, JL and CURZON, P and MARSH, DW and Sentance, S},
	Booktitle = {ACM ISBN 978-1-4503-6588-8/18/10},
	Conference = {WiPSCE 2018 (13th Workshop in Primary and Secondary Computing Education)},
	Date-Added = {2019-03-09 15:34:15 +0000},
	Date-Modified = {2019-03-09 15:34:15 +0000},
	Day = {4},
	Doi = {10.1145/3265757.3265761},
	Editor = {Muhling, A and Cutts, Q and Schwill, A},
	Finishday = {6},
	Finishmonth = {Oct},
	Finishyear = {2018},
	Howpublished = {Online},
	Isbn = {978-1-4503-6588-8},
	Keyword = {Design},
	Month = {Oct},
	Organization = {Potsdam, Germany},
	Publicationstatus = {accepted},
	Startday = {4},
	Startmonth = {Oct},
	Startyear = {2018},
	Title = {Comparing K-5 teachers' reported use of design in teaching programming and planning in teaching writing},
	Year = {2018},
	Bdsk-Url-1 = {https://doi.org/10.1145/3265757.3265761}}



@inproceedings{Evangelia-Kyrimi:2016wd,
	Abstract = {Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support.
},
	Author = {Kyrimi, Evangelia and Marsh, William},
	Booktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},
	Date-Added = {2016-11-05 18:14:43 +0000},
	Date-Modified = {2016-11-05 23:26:54 +0000},
	Pages = {275--286},
	Title = {A Progressive Explanation of Inference in `Hybrid' Bayesian Networks for Supporting Clinical Decision Making},
	Url = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf},
	Year = {2016},
	Bdsk-Url-1 = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf}}



@article{Perkins2015,
	Abstract = {
Background
Lower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT.

Methods
A systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor.

Results
Forty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation.

Conclusion
A significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling.
},
	Author = {Perkins, Z. B. and Yet, B. and Glasgow, S. and Cole, E. and Marsh, W. and Brohi, K. and Rasmussen, T. E. and Tai, N. R. M.},
	Date-Added = {2016-11-05 17:30:11 +0000},
	Date-Modified = {2016-11-05 23:33:25 +0000},
	Doi = {10.1002/bjs.9689},
	Issn = {1365-2168},
	Journal = {British Journal of Surgery},
	Number = {5},
	Pages = {436--450},
	Publisher = {John Wiley & Sons, Ltd},
	Title = {Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma},
	Url = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf},
	Volume = {102},
	Year = {2015},
	Bdsk-Url-1 = {http://dx.doi.org/10.1002/bjs.9689},
	Bdsk-Url-2 = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf}}



@article{Fenton201639,
	Abstract = {Abstract We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed `solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original \{BN\} nodes unchanged. },
	Author = {Norman Fenton and Martin Neil and David Lagnado and William Marsh and Barbaros Yet and Anthony Constantinou},
	Date-Added = {2016-10-24 22:12:29 +0000},
	Date-Modified = {2016-11-05 23:32:43 +0000},
	Doi = {10.1016/j.knosys.2016.09.012},
	Issn = {0950-7051},
	Journal = {Knowledge-Based Systems},
	Keywords = {Uncertain reasoning},
	Pages = {39 - 50},
	Title = {How to model mutually exclusive events based on independent causal pathways in Bayesian network models},
	Url = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},
	Volume = {113},
	Year = {2016},
	Bdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.knosys.2016.09.012}}



@article{Marsh:2016nr,
	Abstract = {In many organisations, large databases are created as part of the business operation: the promise of ?big data? is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts? knowledge is vital for the model?s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.},
	Annote = {doi: 10.1080/09617353.2016.1148923},
	Author = {Marsh, William and Nur, Khalid and Yet, Barbaros and Majumdar, Arnab},
	Booktitle = {Safety and Reliability},
	Date = {2016/01/02},
	Date-Added = {2016-10-24 22:06:10 +0000},
	Date-Modified = {2016-11-05 23:32:15 +0000},
	Doi = {10.1080/09617353.2016.1148923},
	Isbn = {0961-7353},
	Journal = {Safety and Reliability},
	Journal1 = {Safety and Reliability},
	Month = {jan},
	Number = {1},
	Pages = {35--47},
	Publisher = {Taylor \& Francis},
	Title = {Using operational data for decision making: a feasibility study in rail maintenance},
	Url = {http://dx.doi.org/10.1080/09617353.2016.1148923},
	Volume = {36},
	Year = {2016},
	Year1 = {2016},
	Bdsk-Url-1 = {http://dx.doi.org/10.1080/09617353.2016.1148923}}



@inproceedings{Marsh2009,
	Author = {Marsh, D. William R. and Bearfield, George J.},
	Booktitle = {Proceedings of the Ninth International Scientific School MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg, Russia)},
	Date-Added = {2016-02-20 17:11:29 +0000},
	Date-Modified = {2016-02-20 17:14:07 +0000},
	Month = {July},
	Title = {Why Risk Models should be Parameterised.},
	Year = {2009}}



@inproceedings{Dray2007,
	Author = {Dray, Peter and Bearfield, George J. and Marsh, D William R.},
	Booktitle = {Proceedings of 25th International System Safety Conference, Baltimore, USA},
	Date-Added = {2016-02-20 16:34:41 +0000},
	Date-Modified = {2016-11-06 19:59:37 +0000},
	Month = {August},
	Title = {Constructing Scalable and Parameterised System Wide Risk Models},
	Year = {2007}}



@article{wichmann1995industrial,
	Author = {Wichmann, BA and Canning, AA and Clutterbuck, DL and Winsborrow, LA and Ward, NJ and Marsh, DWR},
	Journal = {Software Engineering Journal},
	Number = {2},
	Pages = {69},
	Publisher = {London: published by the Institution of Electrical Engineers for the British Computer Society and the Institution of Electrical Engineers, c1986-c1996.},
	Title = {Industrial perspective on static analysis},
	Volume = {10},
	Year = {1995}}



@article{marsh2008generalizing,
	Author = {Marsh, DWR and Bearfield, G},
	Date-Modified = {2016-11-06 19:55:16 +0000},
	Doi = {10.1243/1748006XJRR131},
	Journal = {Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability},
	Number = {2},
	Pages = {105--114},
	Publisher = {SAGE Publications},
	Title = {Generalizing event trees using Bayesian networks},
	Volume = {222},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1243/1748006XJRR131}}



@incollection{bearfield2005generalising,
	Author = {Bearfield, George and Marsh, William},
	Booktitle = {Proceedings of 24th International Conference, SAFECOMP 2005, Computer Safety, Reliability, and Security},
	Date-Modified = {2016-02-20 17:44:15 +0000},
	Pages = {52--66},
	Publisher = {Springer Berlin Heidelberg},
	Series = {Lecture Notes in Computer Science},
	Title = {Generalising event trees using Bayesian networks with a case study of train derailment},
	Volume = {3688},
	Year = {2005}}



@incollection{marsh2004using,
	Author = {Marsh, William and Bearfield, George},
	Booktitle = {Probabilistic Safety Assessment and Management},
	Pages = {3597--3602},
	Publisher = {Springer London},
	Title = {Using Bayesian networks to model accident causation in the UK railway industry},
	Year = {2004}}



@inproceedings{marsh2007representing,
	Author = {Marsh, William and Bearfield, George},
	Booktitle = {SAFECOMP'07 26th international conference on Computer Safety, Reliability, and Security},
	Date-Modified = {2016-11-06 20:06:31 +0000},
	Doi = {10.1007/978-3-540-75101-4},
	Organization = {Springer Berlin Heidelberg},
	Pages = {120--133},
	Title = {Representing parameterised fault trees using Bayesian networks},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-540-75101-4}}



@inproceedings{bearfield2010causal,
	Abstract = {The Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk.},
	Author = {Bearfield, George and Marsh, William},
	Booktitle = {Ale, B, Papazuglo, I \& Zio, E (eds) Back to the future. Proceedings of the European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, 5-9 September 2010. ISBN 0415604273},
	Date-Modified = {2016-11-05 17:57:28 +0000},
	Publisher = {CRC Press Inc},
	Title = {Causal Modelling of Lower Consequence Rail Safety Incidents},
	Url = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2},
	Year = {2010},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2}}



@inproceedings{fenton2004making,
	Author = {Fenton, Norman and Marsh, William and Neil, Martin and Cates, Patrick and Forey, Simon and Tailor, Manesh},
	Booktitle = {Software Engineering, 2004. ICSE 2004. Proceedings. 26th International Conference on},
	Organization = {IEEE},
	Pages = {397--406},
	Title = {Making resource decisions for software projects},
	Year = {2004}}



@article{fenton2006predicting,
	Author = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Marquez, David and Krause, Paul and Mishra, Rajat},
	Date-Modified = {2016-11-06 20:31:20 +0000},
	Doi = {10.1016/j.infsof.2006.09.001},
	Journal = {Information and Software Technology},
	Number = {1},
	Pages = {32--43},
	Publisher = {Elsevier},
	Title = {Predicting software defects in varying development lifecycles using Bayesian nets},
	Url = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},
	Volume = {49},
	Year = {2006},
	Bdsk-Url-1 = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.infsof.2006.09.001}}



@article{fenton2008effectiveness,
	Author = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radli{\'n}ski, {\L}ukasz and Krause, Paul},
	Date-Modified = {2016-11-06 19:58:08 +0000},
	Doi = {10.1007/s10664-008-9072-x},
	Journal = {Empirical Software Engineering},
	Number = {5},
	Pages = {499--537},
	Publisher = {Springer US},
	Title = {On the effectiveness of early life cycle defect prediction with Bayesian Nets},
	Volume = {13},
	Year = {2008},
	Bdsk-Url-1 = {http://dx.doi.org/10.1007/s10664-008-9072-x}}



@inproceedings{fenton2007project,
	Author = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radlinski, Lukasz and Krause, Paul},
	Booktitle = {Proceeding PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering},
	Date-Modified = {2016-11-06 20:24:32 +0000},
	Doi = {10.1109/PROMISE.2007.11},
	Keywords = {Software projects},
	Organization = {IEEE},
	Pages = {2--11},
	Title = {Project data incorporating qualitative factors for improved software defect prediction},
	Year = {2007},
	Bdsk-Url-1 = {http://dx.doi.org/10.1109/PROMISE.2007.11}}



@article{yet2013decision,
	Author = {Yet, Barbaros and Bastani, Kaveh and Raharjo, Hendry and Lifvergren, Svante and Marsh, William and Bergman, Bo},
	Date-Modified = {2016-11-05 23:36:58 +0000},
	Doi = {10.1016/j.dss.2012.10.007},
	Journal = {Decision Support Systems},
	Number = {2},
	Pages = {488--498},
	Publisher = {North-Holland},
	Title = {Decision support system for Warfarin therapy management using Bayesian networks},
	Volume = {55},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.dss.2012.10.007}}



@article{yet2011towards,
	Abstract = {We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model confiorms to the clinician's way of reasoning and so that we can predict the probable effect of the available interventions. Since we cannot always depend on data from controlled trials, we depend instead on 'clinical knowledge' and it is therefore vital that this elicited rigorously. We propose three stages of knowledge modeling covering the treatment process, the information generated by the process and the causal relationship. These stages lead to a causal Bayesian network, which is used to predict the patient outcome under different treatment options.},
	Author = {Yet, Barbaros and Perkins, Zane and Marsh, William and Fenton, Norman},
	Date-Modified = {2016-11-05 17:51:59 +0000},
	Journal = {Probabilistic Problem Solving in BioMedicine},
	Pages = {107--120},
	Title = {Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support},
	Url = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2},
	Year = {2011},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2}}



@article{holloway2013change,
	Author = {Holloway, Anna and Marsh, William and Bearfield, George},
	Date-Modified = {2016-11-05 23:37:33 +0000},
	Doi = {10.1177/0954409713498381},
	Journal = {Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit},
	Number = {6},
	Pages = {704--714},
	Publisher = {Sage Publications},
	Title = {Change and safety: decision-making from data},
	Volume = {227},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1177/0954409713498381}}



@inproceedings{carre1992spark,
	Author = {Carre, Bernard and Garnsworthy, Jon and Marsh, William},
	Booktitle = {Ada in transition: Proceedings of the 1992 Ada UK International Conference},
	Date-Modified = {2016-11-06 22:36:19 +0000},
	Editor = {Taylor, W J},
	Month = {Oct},
	Number = {4},
	Pages = {31--45},
	Publisher = {IOS Press},
	Series = {Studies in computer and communications systems},
	Title = {SPARK---a safetyrelated Ada subset},
	Url = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK---a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false},
	Year = {1992},
	Bdsk-Url-1 = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&lpg=PA31&ots=oS691rW-Vy&dq=SPARK---a%20safety%20related%20Ada%20subset.&pg=PA31#v=onepage&q&f=false}}



@article{marsh1997harmonisation,
	Author = {Marsh, W},
	Journal = {Microprocessors and Microsystems},
	Number = {1},
	Pages = {41--47},
	Publisher = {Elsevier},
	Title = {Harmonisation of defence standards for safety-critical software},
	Volume = {21},
	Year = {1997}}



@article{yet2014not,
	Abstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},
	Author = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},
	Date-Modified = {2016-11-05 23:33:44 +0000},
	Doi = {10.1016/j.jbi.2013.10.012},
	Journal = {Journal of biomedical informatics},
	Pages = {28--37},
	Publisher = {Academic Press},
	Title = {Not just data: A method for improving prediction with knowledge},
	Volume = {48},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2013.10.012}}



@article{yet2014compatible,
	Abstract = {The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four `abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.},
	Author = {Yet, Barbaros and Marsh, D William R},
	Date-Modified = {2016-11-05 23:34:05 +0000},
	Doi = {10.1016/j.knosys.2014.02.020},
	Journal = {Knowledge-Based Systems},
	Pages = {84--97},
	Publisher = {Elsevier},
	Title = {Compatible and incompatible abstractions in Bayesian networks},
	Volume = {62},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.knosys.2014.02.020}}



@article{yet2014combining,
	Abstract = {Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report `univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model -- ignoring some complexities of the problem -- or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.},
	Author = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel RM and Marsh, D William R},
	Date-Modified = {2016-11-05 23:34:14 +0000},
	Doi = {10.1016/j.jbi.2014.07.018},
	Journal = {Journal of biomedical informatics},
	Pages = {373--385},
	Publisher = {Academic Press},
	Title = {Combining data and meta-analysis to build Bayesian networks for clinical decision support},
	Volume = {52},
	Year = {2014},
	Bdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2014.07.018}}



@article{constantinou2015risk,
	Abstract = {Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management -- Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.},
	Author = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Fenton, Norman and Coid, Jeremy},
	Date-Modified = {2016-11-05 23:33:14 +0000},
	Doi = {10.1016/j.eswa.2015.05.025},
	Journal = {Expert Systems with Applications},
	Number = {21},
	Pages = {7511--7529},
	Publisher = {Pergamon},
	Title = {Risk assessment and risk management of violent reoffending among prisoners},
	Url = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},
	Volume = {42},
	Year = {2015},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.eswa.2015.05.025}}



@article{marsh2007merging,
	Author = {Marsh, DWR and Bearfield, GJ},
	Date-Modified = {2016-02-20 17:16:42 +0000},
	Journal = {Proceedings of ESREL 2007, Stavanger, Norway},
	Month = {June},
	Title = {Merging Event Trees using Bayesian Networks},
	Year = {2007}}



@article{constantinou2015causal,
	Abstract = {The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.},
	Author = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Coid, Jeremy},
	Date-Modified = {2016-11-05 23:33:35 +0000},
	Doi = {10.1016/j.dss.2015.09.006},
	Journal = {Decision Support Systems},
	Pages = {42--55},
	Publisher = {North-Holland},
	Title = {Causal inference for violence risk management and decision support in forensic psychiatry},
	Url = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},
	Volume = {80},
	Year = {2015},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.dss.2015.09.006}}



@article{constantinou2015value,
	Abstract = {Objectives
Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.

Method
The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks.

Results
The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%).

Conclusions
We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.},
	Author = {Constantinou, Anthony Costa and Yet, Barbaros and Fenton, Norman and Neil, Martin and Marsh, William},
	Date-Modified = {2016-11-05 23:30:54 +0000},
	Doi = {10.1016/j.artmed.2015.09.002},
	Journal = {Artificial Intelligence in Medicine},
	Pages = {41--52},
	Title = {Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences},
	Url = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},
	Volume = {66},
	Year = {2016},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2015.09.002}}



@inproceedings{winther2013hazards,
	Author = {Winther, Rune and Marsh, William},
	Booktitle = {ESREL 2013. Safety, Reliability and Risk Analysis: Beyond the Horizon},
	Date-Modified = {2016-11-05 23:36:02 +0000},
	Doi = {10.1201/b15938-381},
	Organization = {CRC Press},
	Pages = {2545--2553},
	Title = {Hazards, accidents and events---a land of confusing terms},
	Year = {2013},
	Bdsk-Url-1 = {http://dx.doi.org/10.1201/b15938-381}}



@article{yet2014explicit,
	Abstract = {Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.},
	Author = {Yet, Barbaros and Perkins, Zane and Tai, Nigel and Marsh, William},
	Date-Modified = {2016-11-05 17:42:22 +0000},
	Doi = {10.3233/978-1-61499-432-9-53},
	Journal = {Studies in Health Technology and Informatics},
	Pages = {53--7},
	Title = {Explicit evidence for prognostic Bayesian network models},
	Volume = {205},
	Year = {2014},
	Bdsk-File-1 = {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},
	Bdsk-Url-1 = {https://doi.org/10.3233/978-1-61499-432-9-53}}



@article{constantinou2016complex,
	Abstract = {OBJECTIVES: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.},
	Author = {Constantinou, Anthony Costa and Fenton, Norman and Marsh, William and Radlinski, Lukasz},
	Date-Modified = {2016-11-05 23:32:53 +0000},
	Doi = {10.1016/j.artmed.2016.01.002},
	Journal = {Artificial Intelligence in Medicine},
	Pages = {75-93},
	Title = {From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support},
	Url = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},
	Volume = {67},
	Year = {2016},
	Bdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},
	Bdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2016.01.002}}
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[link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-marsh-morrissey-towardsanevidencebaseddecisionsupporttoolformanagementofmusculoskeletalconditions-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-marsh-morrissey-towardsanevidencebaseddecisionsupporttoolformanagementofmusculoskeletalconditions-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2018towardsconditions,\n\tAbstract = {Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model&#x27;s performance.},\n\tAuthor = {Yet, B and Marsh, W and Morrissey, D},\n\tDate-Added = {2019-03-09 15:34:38 +0000},\n\tDate-Modified = {2019-03-09 15:34:38 +0000},\n\tEissn = {1879-8365},\n\tJournal = {Stud Health Technol Inform},\n\tKeyword = {musculoskeletal},\n\tLanguage = {eng},\n\tOrganization = {Netherlands},\n\tPages = {175--179},\n\tPublicationstatus = {published},\n\tTitle = {Towards an Evidence-Based Decision Support Tool for Management of Musculoskeletal Conditions.},\n\tUrl = {https://www.ncbi.nlm.nih.gov/pubmed/30306931},\n\tVolume = {255},\n\tYear = {2018},\n\tBdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/30306931}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Musculoskeletal (MSK) problems present an increasing burden for the healthcare sector, particularly in ageing populations. Advances in evidence are often slow to influence clinical decisions, suggesting decision support would be beneficial. We propose a Bayesian network (BN) for providing evidence-based decision support as it can explicitly represent domain knowledge as causal relations and allows both domain knowledge and clinical data to be combined to create a usable decision model. We make a preliminary evaluation of the model's performance.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.ijcses.org/index.php/ijcses/index\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018', 'http://www.ijcses.org/index.php/ijcses/index', event);\">\n    Abstraction in action: K-5 teachers' uses of levels of abstraction, particularly the design level, in teaching programming.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  WAITE, J.; CURZON, P; MARSH, D; Sentance, S;  and Hawden-Bennett, A\n</span>\n\n  \n\n</span>\n\n  <i>International Journal Of Computer Science Education In Schools</i>, 2(2): 14–40. Jan 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.ijcses.org/index.php/ijcses/index\"\n     onclick=\"log_download('waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018', 'http://www.ijcses.org/index.php/ijcses/index', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Abstraction in action: K-5 teachers&#x27; uses of levels of abstraction, particularly the design level, in teaching programming [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.21585/ijcses.v2i1.23\"\n     onclick=\"log_download('waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018', 'http://doi.org/10.21585/ijcses.v2i1.23', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('waite-curzon-marsh-sentance-hawdenbennett-abstractioninactionk5teachersusesoflevelsofabstractionparticularlythedesignlevelinteachingprogramming-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{waite2018abstractionprogramming,\n\tAbstract = {Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess &#x60;do-ability&#x27;. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers&#x27;\nknowledge of terms including algorithm and code; a concept of &#x60;emergent algorithms&#x27; is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study&#x27;s results are generalisable more widely.},\n\tAddress = {On line Journal},\n\tAuthor = {WAITE, JL and CURZON, P and MARSH, D and Sentance, S and Hawden-Bennett, A},\n\tDate-Added = {2019-03-09 15:34:34 +0000},\n\tDate-Modified = {2019-03-09 15:34:34 +0000},\n\tDay = {31},\n\tDoi = {10.21585/ijcses.v2i1.23},\n\tEditor = {Kalelioglu, F and Allsop, Y},\n\tIssn = {2513-8359},\n\tIssue = {1},\n\tJournal = {International Journal Of Computer Science Education In Schools},\n\tKeyword = {Computing Eductation},\n\tMonth = {Jan},\n\tNumber = {2},\n\tPages = {14--40},\n\tPublicationstatus = {published},\n\tTitle = {Abstraction in action: K-5 teachers&#x27; uses of levels of abstraction, particularly the design level, in teaching programming},\n\tUrl = {http://www.ijcses.org/index.php/ijcses/index},\n\tVolume = {2},\n\tYear = {2018},\n\tBdsk-Url-1 = {http://www.ijcses.org/index.php/ijcses/index},\n\tBdsk-Url-2 = {https://doi.org/10.21585/ijcses.v2i1.23}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Research indicates that understanding levels of abstraction (LOA) and being able to move between the levels is essential to programming success. For K-5 contexts LOA levels have been named: problem, design, code and running the code. In a qualitative exploratory study, five K-5 teachers were interviewed on their uses of LOA, particularly the design level, in teaching programming and other subjects. Using PCK elements to analyse responses, the teachers interviewed used design as an instructional strategy and for assessment. The teachers used design as an aide memoire and the expert teachers used design: as a contract for pair-programming; to work out what they needed to teach; for learners to annotate with code snippets (to transition across LOA); for learners to self-assess and to assess `do-ability'. The teachers used planning in teaching writing to scaffold learning and promote self-regulation revealing their insight in student understanding. One issue was of the teachers' knowledge of terms including algorithm and code; a concept of `emergent algorithms' is proposed. Findings from the study suggest design helps learners learn to program in the same way that planning helps learners learn to write and that LOA, particularly the design level, may provide an accessible exemplar of abstraction in action. Further work is needed to verify whether the study's results are generalisable more widely.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/29717954\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018', 'https://www.ncbi.nlm.nih.gov/pubmed/29717954', event);\">\n    Learning Health Systems: The research community awareness challenge.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  McLachlan, S; Dube, K; Buchanan, D; Lean, S; Johnson, O; Potts, H; Gallagher, T; Marsh, W;  and Fenton, N\n</span>\n\n  \n\n</span>\n\n  <i>J Innov Health Inform</i>, 25: 981. Mar 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.ncbi.nlm.nih.gov/pubmed/29717954\"\n     onclick=\"log_download('mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018', 'https://www.ncbi.nlm.nih.gov/pubmed/29717954', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Learning Health Systems: The research community awareness challenge. [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.14236/jhi.v25i1.981\"\n     onclick=\"log_download('mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018', 'http://doi.org/10.14236/jhi.v25i1.981', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mclachlan-dube-buchanan-lean-johnson-potts-gallagher-marsh-etal-learninghealthsystemstheresearchcommunityawarenesschallenge-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{mclachlan2018learningchallenge,\n\tAuthor = {McLachlan, S and Dube, K and Buchanan, D and Lean, S and Johnson, O and Potts, H and Gallagher, T and Marsh, W and Fenton, N},\n\tDate-Added = {2019-03-09 15:34:29 +0000},\n\tDate-Modified = {2019-03-09 15:34:29 +0000},\n\tDay = {27},\n\tDoi = {10.14236/jhi.v25i1.981},\n\tEissn = {2058-4563},\n\tIssue = {1},\n\tJournal = {J Innov Health Inform},\n\tKeyword = {Learning Health Systems, Electronic Health Records},\n\tLanguage = {eng},\n\tMonth = {Mar},\n\tOrganization = {England},\n\tPages = {981},\n\tPublicationstatus = {online-published},\n\tTitle = {Learning Health Systems: The research community awareness challenge.},\n\tUrl = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},\n\tVolume = {25},\n\tYear = {2018},\n\tBdsk-Url-1 = {https://www.ncbi.nlm.nih.gov/pubmed/29717954},\n\tBdsk-Url-2 = {https://doi.org/10.14236/jhi.v25i1.981}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://haoyuan.uk/\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018', 'http://haoyuan.uk/', event);\">\n    Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  ZHANG, H;  and MARSH, D.\n</span>\n\n  \n\n</span>\n\n  In <i>ESREL2018 conference proceeding</i>, Jun 2018. Trondheim, Norway, Taylor & Francis\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://haoyuan.uk/\"\n     onclick=\"log_download('zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018', 'http://haoyuan.uk/', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('zhang-marsh-towardsamodelbasedassetdeteriorationframeworkrepresentedbyprobabilisticrelationalmodels-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{zhang2018towardsmodels,\n\tAbstract = {Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.},\n\tAuthor = {ZHANG, H and MARSH, DWR},\n\tBooktitle = {ESREL2018 conference proceeding},\n\tConference = {European Safety and Reliability Conference ESREL 2018},\n\tDate-Added = {2019-03-09 15:34:26 +0000},\n\tDate-Modified = {2019-03-09 15:34:26 +0000},\n\tDay = {17},\n\tFinishday = {21},\n\tFinishmonth = {Jun},\n\tFinishyear = {2018},\n\tKeyword = {probabilistic relational model},\n\tMonth = {Jun},\n\tOrganization = {Trondheim, Norway},\n\tPublicationstatus = {accepted},\n\tPublisher = {Taylor \\&amp; Francis},\n\tStartday = {17},\n\tStartmonth = {Jun},\n\tStartyear = {2018},\n\tTitle = {Towards A Model-Based Asset Deterioration Framework Represented by Probabilistic Relational Models},\n\tUrl = {http://haoyuan.uk/},\n\tYear = {2018},\n\tBdsk-Url-1 = {http://haoyuan.uk/}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Most asset deterioration tools are designed for a specific application, as a consequence, a small change of the specification may result in a complete change of the tool. Inspired by the model-based approach of separating problem specification from analysis technique, we propose a model-based asset deterioration assessment framework using probabilistic relational models. The probabilistic relational models express abstract probabilistic dependency covers a range of deterioration modelling assumptions. An expert in the domain of asset deterioration can then use his knowledge of the factors that affect deterioration to customise the abstract models to a specific application, without requiring a detailed understanding the underlying computational framework. We illustrate the use of the framework with multiple variants of deterioration models.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"mclachlan-potts-dube-buchanan-lean-gallagher-johnson-daley-etal-theheimdallframeworkforsupportingcharacterisationoflearninghealthsystems-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  The Heimdall framework for supporting characterisation of learning health systems.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  MCLACHLAN, S; Potts, H.; Dube, K; Buchanan, D; Lean, S; Gallagher, T; Johnson, O; DALEY, B; Marsh, W;  and FENTON, N\n</span>\n\n  \n\n</span>\n\n  <i>BCS Journal of Innovation in Health Informatics</i>, 25. Jun 2018.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.14236/jhi.v25i2.996\"\n     onclick=\"log_download('mclachlan-potts-dube-buchanan-lean-gallagher-johnson-daley-etal-theheimdallframeworkforsupportingcharacterisationoflearninghealthsystems-2018', 'http://doi.org/10.14236/jhi.v25i2.996', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/mclachlan-potts-dube-buchanan-lean-gallagher-johnson-daley-etal-theheimdallframeworkforsupportingcharacterisationoflearninghealthsystems-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('mclachlan-potts-dube-buchanan-lean-gallagher-johnson-daley-etal-theheimdallframeworkforsupportingcharacterisationoflearninghealthsystems-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{mclachlan2018thesystems,\n\tAbstract = {There are many proposed benefits of using learning health systems  (LHS), including improved patient outcomes. There has been little adoption of LHS  in  practice  due  to  challenges  and  barriers  that  limit  adoption  of  new  data-driven  technologies  in  healthcare.  We  have  identified  a  more  fundamental  explanation:  the  majority  of  developments  in  LHS  are  not  identified  as  LHS.  The  absence  of  a  unifying  namespace  and  framework  brings  a  lack  of  consistency  in  how  LHS  are identified and classified. As a result, the LHS &#x60;community&#x27; is fragmented, with  groups working on similar systems being not aware of each other&#x27;s work. This leads  to duplication and the lack of a critical mass of researchers necessary to address  barriers to adoption.\nObjective  To  find  a  way  to  support  easy  identification  and  classification  of  research works within the domain of LHS.\nMethod  A qualitative meta-narrative study focusing on works that self-identified  as LHS was used for two purposes. First, to find existing standard definitions and  frameworks  using  these  to  create  a  new  unifying  framework.  Second,  seeking  whether it was possible to classify those LHS solutions within the new framework. Results  The study found that with apparently limited awareness, all current LHS  works  fall  within  nine  primary  archetypes.  These  findings  were  used  to  develop a  unifying  framework  for  LHS  to  classify  works  as  LHS,  and  reduce  diversity  and  fragmentation within the domain.\nConclusion  Our finding brings clarification where there has been limited aware- ness  for  LHS  among  researchers.  We  believe  our  framework  is  simple  and  may  help researchers to classify works in the LHS domain. This framework may enable  realisation  of  the  critical  mass  necessary  to  bring  more  substantial  collaboration  and funding to LHS. Ongoing research will seek to establish the framework&#x27;s effect  on the LHS domain.},\n\tAuthor = {MCLACHLAN, S and Potts, HWW and Dube, K and Buchanan, D and Lean, S and Gallagher, T and Johnson, O and DALEY, B and Marsh, W and FENTON, N},\n\tDate-Added = {2019-03-09 15:34:21 +0000},\n\tDate-Modified = {2019-03-09 15:34:21 +0000},\n\tDay = {15},\n\tDoi = {10.14236/jhi.v25i2.996},\n\tIssue = {2},\n\tJournal = {BCS Journal of Innovation in Health Informatics},\n\tKeyword = {precision medicine},\n\tMonth = {Jun},\n\tPublicationstatus = {published},\n\tTitle = {The Heimdall framework for supporting characterisation of learning health systems},\n\tVolume = {25},\n\tYear = {2018},\n\tBdsk-Url-1 = {https://doi.org/10.14236/jhi.v25i2.996}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  There are many proposed benefits of using learning health systems (LHS), including improved patient outcomes. There has been little adoption of LHS in practice due to challenges and barriers that limit adoption of new data-driven technologies in healthcare. We have identified a more fundamental explanation: the majority of developments in LHS are not identified as LHS. The absence of a unifying namespace and framework brings a lack of consistency in how LHS are identified and classified. As a result, the LHS `community' is fragmented, with groups working on similar systems being not aware of each other's work. This leads to duplication and the lack of a critical mass of researchers necessary to address barriers to adoption. Objective To find a way to support easy identification and classification of research works within the domain of LHS. Method A qualitative meta-narrative study focusing on works that self-identified as LHS was used for two purposes. First, to find existing standard definitions and frameworks using these to create a new unifying framework. Second, seeking whether it was possible to classify those LHS solutions within the new framework. Results The study found that with apparently limited awareness, all current LHS works fall within nine primary archetypes. These findings were used to develop a unifying framework for LHS to classify works as LHS, and reduce diversity and fragmentation within the domain. Conclusion Our finding brings clarification where there has been limited aware- ness for LHS among researchers. We believe our framework is simple and may help researchers to classify works in the LHS domain. This framework may enable realisation of the critical mass necessary to bring more substantial collaboration and funding to LHS. Ongoing research will seek to establish the framework's effect on the LHS domain.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"waite-curzon-marsh-sentance-comparingk5teachersreporteduseofdesigninteachingprogrammingandplanninginteachingwriting-2018\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Comparing K-5 teachers' reported use of design in teaching programming and planning in teaching writing.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  WAITE, J.; CURZON, P; MARSH, D.;  and Sentance, S\n</span>\n\n  \n\n</span>\n\n  In Muhling, A; Cutts, Q;  and Schwill, A, editor(s), <i>ACM ISBN 978-1-4503-6588-8/18/10</i>, Oct 2018. Potsdam, Germany\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1145/3265757.3265761\"\n     onclick=\"log_download('waite-curzon-marsh-sentance-comparingk5teachersreporteduseofdesigninteachingprogrammingandplanninginteachingwriting-2018', 'http://doi.org/10.1145/3265757.3265761', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/waite-curzon-marsh-sentance-comparingk5teachersreporteduseofdesigninteachingprogrammingandplanninginteachingwriting-2018\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('waite-curzon-marsh-sentance-comparingk5teachersreporteduseofdesigninteachingprogrammingandplanninginteachingwriting-2018')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{waite2018comparingwriting,\n\tAbstract = {K-5 teachers teach a range of subjects \\&amp; develop generic teaching\nskills; when starting to teach computing, particularly programming,\npractitioners may not realise that they can draw on these other\nskills to support their teaching. In a small study of K-5 teachers,\npotential synergies were suggested between using planning in the\nthe teaching of writing and design in the teaching of programming.\nIn this paper, we explore these synergies by surveying a wider group\nof teachers (n=207) on their uses of planning and design. Teachers\nreported the usefulness of planning for writing and design for\nprogramming as equally important. However, there were significant\ndifferences in their uses. The majority saw planning as essential in\nwriting \\&amp; put this into practice in their teaching. For example, they\ndemonstrated the creation of plans, expected students to annotate\nplans, required students to refer to plans when writing and used\nplans to differentiate. By contrast, these uses were implemented less\nfrequently in programming tasks. We also report on differences in\nthe confidence of male \\&amp; female respondents, \\&amp; between generalists\n(who teach programming \\&amp; writing) \\&amp; specialists (who do not\nteach writing). For example, females were more confident to teach\nwriting than programming, with males vice versa. Having revealed\nopportunities for reuse of successful techniques used in teaching\nwriting for the teaching of programming we recommend further\nwork is needed to explore this transfer of pedagogical knowledge.},\n\tAuthor = {WAITE, JL and CURZON, P and MARSH, DW and Sentance, S},\n\tBooktitle = {ACM ISBN 978-1-4503-6588-8/18/10},\n\tConference = {WiPSCE 2018 (13th Workshop in Primary and Secondary Computing Education)},\n\tDate-Added = {2019-03-09 15:34:15 +0000},\n\tDate-Modified = {2019-03-09 15:34:15 +0000},\n\tDay = {4},\n\tDoi = {10.1145/3265757.3265761},\n\tEditor = {Muhling, A and Cutts, Q and Schwill, A},\n\tFinishday = {6},\n\tFinishmonth = {Oct},\n\tFinishyear = {2018},\n\tHowpublished = {Online},\n\tIsbn = {978-1-4503-6588-8},\n\tKeyword = {Design},\n\tMonth = {Oct},\n\tOrganization = {Potsdam, Germany},\n\tPublicationstatus = {accepted},\n\tStartday = {4},\n\tStartmonth = {Oct},\n\tStartyear = {2018},\n\tTitle = {Comparing K-5 teachers&#x27; reported use of design in teaching programming and planning in teaching writing},\n\tYear = {2018},\n\tBdsk-Url-1 = {https://doi.org/10.1145/3265757.3265761}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  K-5 teachers teach a range of subjects & develop generic teaching skills; when starting to teach computing, particularly programming, practitioners may not realise that they can draw on these other skills to support their teaching. In a small study of K-5 teachers, potential synergies were suggested between using planning in the the teaching of writing and design in the teaching of programming. In this paper, we explore these synergies by surveying a wider group of teachers (n=207) on their uses of planning and design. Teachers reported the usefulness of planning for writing and design for programming as equally important. However, there were significant differences in their uses. The majority saw planning as essential in writing & put this into practice in their teaching. For example, they demonstrated the creation of plans, expected students to annotate plans, required students to refer to plans when writing and used plans to differentiate. By contrast, these uses were implemented less frequently in programming tasks. We also report on differences in the confidence of male & female respondents, & between generalists (who teach programming & writing) & specialists (who do not teach writing). For example, females were more confident to teach writing than programming, with males vice versa. Having revealed opportunities for reuse of successful techniques used in teaching writing for the teaching of programming we recommend further work is needed to explore this transfer of pedagogical knowledge.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2017\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2017')\"\n      onkeypress=\"toggleGroup('group_2017')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2017</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yet-perkins-tai-marsh-clinicalevidenceframeworkforbayesiannetworks-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Clinical evidence framework for Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Perkins, Z. B.; Tai, N. R. M.;  and Marsh, D. W. R.\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge and Information Systems</i>,1–27. jan 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10115-016-0932-1\"\n     onclick=\"log_download('yet-perkins-tai-marsh-clinicalevidenceframeworkforbayesiannetworks-2017', 'http://doi.org/10.1007/s10115-016-0932-1', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-tai-marsh-clinicalevidenceframeworkforbayesiannetworks-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-tai-marsh-clinicalevidenceframeworkforbayesiannetworks-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Yet2017,\n\tAbstract = {There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.},\n\tAuthor = {Yet, Barbaros and Perkins, Zane B. and Tai, Nigel R. M. and Marsh, D. William R.},\n\tDate-Added = {2019-03-09 15:35:24 +0000},\n\tDate-Modified = {2019-03-09 15:35:24 +0000},\n\tDoi = {10.1007/s10115-016-0932-1},\n\tIssn = {0219-3116},\n\tJournal = {Knowledge and Information Systems},\n\tMonth = {jan},\n\tPages = {1--27},\n\tTitle = {Clinical evidence framework for Bayesian networks},\n\tYear = {2017},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1007/s10115-016-0932-1}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  There is poor uptake of prognostic decision support models by clinicians regardless of their accuracy. There is evidence that this results from doubts about the basis of the model as the evidence behind clinical models is often not clear to anyone other than their developers. In this paper, we propose a framework for representing the evidence-base of a Bayesian network (BN) decision support model. The aim of this evidence framework is to be able to present all the clinical evidence alongside the BN itself. The evidence framework is capable of presenting supporting and conflicting evidence, and evidence associated with relevant but excluded factors. It also allows the completeness of the evidence to be queried. We illustrate this framework using a BN that has been previously developed to predict acute traumatic coagulopathy, a potentially fatal disorder of blood clotting, at early stages of trauma care.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"marsh-zhang-bayesiannetworkmodelsformakingmaintenancedecisionsfromdataandexpertjudgement-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgement.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, D. W. R.;  and Zhang, H.\n</span>\n\n  \n\n</span>\n\n  In Walls, L.; Revie, M.;  and Bedford, T., editor(s), <i>Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)</i>, pages 1056-1063, Sep 2017. CRC Press Reference - 486 Pages CRC Press ISBN 9781138029972\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-zhang-bayesiannetworkmodelsformakingmaintenancedecisionsfromdataandexpertjudgement-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-zhang-bayesiannetworkmodelsformakingmaintenancedecisionsfromdataandexpertjudgement-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Marsh:2017aa,\n\tAbstract = {To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.},\n\tAuthor = {Marsh, D. W. R. and Zhang, H.},\n\tBooktitle = {Risk, Reliability and Safety: Innovating Theory and Practice: Proceedings of ESREL 2016 (Glasgow, Scotland, 25-29 September 2016)},\n\tDate-Added = {2019-03-09 15:34:48 +0000},\n\tDate-Modified = {2019-03-09 15:34:48 +0000},\n\tEditor = {Lesley Walls and Matthew Revie and Tim Bedford},\n\tMonth = {Sep},\n\tPages = {1056-1063},\n\tPublisher = {CRC Press Reference - 486 Pages CRC Press ISBN 9781138029972},\n\tTitle = {{Bayesian Network Models for Making Maintenance Decisions from Data and Expert Judgement}},\n\tYear = {2017}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  To maximize asset reliability cost-effectively, maintenance should be scheduled based on the likely deterioration of an asset. A number of types of statistical model have been proposed for predicting this but they have important practical limitations. We present a Bayesian network model that can be used for maintenance decision support to overcome these limitations. The model extends an existing statistical model of asset deterioration, but shows how i) failure data from related groups of asset can be combined, ii) data on the condition of assets available from their periodic inspection can be used iii) expert knowledge of the causes deterioration can be combined with statistical data to adjust predictions and iv) the uncertain effects of maintenance actions can be modelled. We show how the model could be used for a range of decision problems, given typical data likely to be available in practice.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"waite-curzon-marsh-sentance-k5teachersusesoflevelsofabstractionfocusingondesign-2017\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  K-5 Teachers' Uses of Levels of Abstraction Focusing on Design.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  WAITE, J.; curzon , P; marsh , D;  and Sentance, S\n</span>\n\n  \n\n</span>\n\n  Nov 2017.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/waite-curzon-marsh-sentance-k5teachersusesoflevelsofabstractionfocusingondesign-2017\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('waite-curzon-marsh-sentance-k5teachersusesoflevelsofabstractionfocusingondesign-2017')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@misc{waite2017k5design,\n\tAbstract = {Recent research with middle school and university students highlights two factors that contribute to programming success: 1) understanding the level of abstraction that you are working at, and 2) being able to move between levels. In this qualitative study, we explored levels of abstraction, and particularly the design level,with five K-5 teachers. Here we outline 11 main findings. The teachers interviewed use the design level for both programming and writing. However, the two expert computing teachers have a far greater depth of understanding of the opportunities for the use of the design level, supporting pupils to understand the level they are\nworking at and helping them move between levels of abstraction by using designs in novel ways. Further work is needed to investigate whether our results are generalisable. Further exploration of levels of abstraction and particularly how the design level helps K-5 learners learn to program, in the same way, that planning supports novices learning to write, is warranted.},\n\tAuthor = {WAITE, JL and curzon, P and marsh, D and Sentance, S},\n\tConference = {WiPSCE 2017},\n\tDate-Added = {2019-03-09 15:34:42 +0000},\n\tDate-Modified = {2019-03-09 15:34:42 +0000},\n\tDay = {9},\n\tKeyword = {Design},\n\tMonth = {Nov},\n\tTitle = {K-5 Teachers&#x27; Uses of Levels of Abstraction Focusing on Design},\n\tYear = {2017}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Recent research with middle school and university students highlights two factors that contribute to programming success: 1) understanding the level of abstraction that you are working at, and 2) being able to move between levels. In this qualitative study, we explored levels of abstraction, and particularly the design level,with five K-5 teachers. Here we outline 11 main findings. The teachers interviewed use the design level for both programming and writing. However, the two expert computing teachers have a far greater depth of understanding of the opportunities for the use of the design level, supporting pupils to understand the level they are working at and helping them move between levels of abstraction by using designs in novel ways. Further work is needed to investigate whether our results are generalisable. Further exploration of levels of abstraction and particularly how the design level helps K-5 learners learn to program, in the same way, that planning supports novices learning to write, is warranted.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2016\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2016')\"\n      onkeypress=\"toggleGroup('group_2016')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2016</span>\n      <span class=\"bibbase_group_count\">\n        \n        (5)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016', 'http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf', event);\">\n    A Progressive Explanation of Inference in `Hybrid' Bayesian Networks for Supporting Clinical Decision Making.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Kyrimi, E.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Eighth International Conference on Probabilistic Graphical Models</i>, pages 275–286,  2016. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf\"\n     onclick=\"log_download('kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016', 'http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"A Progressive Explanation of Inference in &#x60;Hybrid&#x27; Bayesian Networks for Supporting Clinical Decision Making [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('kyrimi-marsh-aprogressiveexplanationofinferenceinhybridbayesiannetworksforsupportingclinicaldecisionmaking-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Evangelia-Kyrimi:2016wd,\n\tAbstract = {Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support.\n},\n\tAuthor = {Kyrimi, Evangelia and Marsh, William},\n\tBooktitle = {Proceedings of the Eighth International Conference on Probabilistic Graphical Models},\n\tDate-Added = {2016-11-05 18:14:43 +0000},\n\tDate-Modified = {2016-11-05 23:26:54 +0000},\n\tPages = {275--286},\n\tTitle = {A Progressive Explanation of Inference in &#x60;Hybrid&#x27; Bayesian Networks for Supporting Clinical Decision Making},\n\tUrl = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf},\n\tYear = {2016},\n\tBdsk-Url-1 = {http://www.jmlr.org/proceedings/papers/v52/kyrimi16.pdf}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many Bayesian networks (BNs) have been developed as decision support tools. However, far fewer have been used in practice. Sometimes it is assumed that an accurate prediction is enough for useful decision support but this neglects the importance of trust: a user who does not trust a tool will not accept its advice. Giving users an explanation of the way a BN reasons may make its predictions easier to trust. In this study, we propose a progressive explanation of inference that can be applied to any hybrid BN. The key questions that we answer are: which important evidence supports or contradicts the prediction and through which intermediate variables does the evidence flow. The explanation is illustrated using different scenarios in a BN designed for medical decision support. \n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S095070511630329X\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016', 'http://www.sciencedirect.com/science/article/pii/S095070511630329X', event);\">\n    How to model mutually exclusive events based on independent causal pathways in Bayesian network models.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fenton, N.; Neil, M.; Lagnado, D.; Marsh, W.; Yet, B.;  and Constantinou, A.\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge-Based Systems</i>, 113: 39 - 50.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://www.sciencedirect.com/science/article/pii/S095070511630329X\"\n     onclick=\"log_download('fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016', 'http://www.sciencedirect.com/science/article/pii/S095070511630329X', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"How to model mutually exclusive events based on independent causal pathways in Bayesian network models [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.knosys.2016.09.012\"\n     onclick=\"log_download('fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016', 'http://doi.org/10.1016/j.knosys.2016.09.012', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fenton-neil-lagnado-marsh-yet-constantinou-howtomodelmutuallyexclusiveeventsbasedonindependentcausalpathwaysinbayesiannetworkmodels-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Fenton201639,\n\tAbstract = {Abstract We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed &#x60;solution&#x27;, which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original \\{BN\\} nodes unchanged. },\n\tAuthor = {Norman Fenton and Martin Neil and David Lagnado and William Marsh and Barbaros Yet and Anthony Constantinou},\n\tDate-Added = {2016-10-24 22:12:29 +0000},\n\tDate-Modified = {2016-11-05 23:32:43 +0000},\n\tDoi = {10.1016/j.knosys.2016.09.012},\n\tIssn = {0950-7051},\n\tJournal = {Knowledge-Based Systems},\n\tKeywords = {Uncertain reasoning},\n\tPages = {39 - 50},\n\tTitle = {How to model mutually exclusive events based on independent causal pathways in Bayesian network models},\n\tUrl = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},\n\tVolume = {113},\n\tYear = {2016},\n\tBdsk-Url-1 = {http://www.sciencedirect.com/science/article/pii/S095070511630329X},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.knosys.2016.09.012}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Abstract We show that existing Bayesian network (BN) modelling techniques cannot capture the correct intuitive reasoning in the important case when a set of mutually exclusive events need to be modelled as separate nodes instead of states of a single node. A previously proposed `solution', which introduces a simple constraint node that enforces mutual exclusivity, fails to preserve the prior probabilities of the events, while other proposed solutions involve major changes to the original model. We provide a novel and simple solution to this problem that works in all cases where the mutually exclusive nodes have no common ancestors. Our solution uses a special type of constraint and auxiliary node together with formulas for assigning their necessary conditional probability table values. The solution enforces mutual exclusivity between events and preserves their prior probabilities while leaving all original \\BN\\ nodes unchanged. \n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"http://dx.doi.org/10.1080/09617353.2016.1148923\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016', 'http://dx.doi.org/10.1080/09617353.2016.1148923', event);\">\n    Using operational data for decision making: a feasibility study in rail maintenance.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, W.; Nur, K.; Yet, B.;  and Majumdar, A.\n</span>\n\n  \n\n</span>\n\n  <i>Safety and Reliability</i>, 36(1): 35–47. jan 2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"http://dx.doi.org/10.1080/09617353.2016.1148923\"\n     onclick=\"log_download('marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016', 'http://dx.doi.org/10.1080/09617353.2016.1148923', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Using operational data for decision making: a feasibility study in rail maintenance [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1080/09617353.2016.1148923\"\n     onclick=\"log_download('marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016', 'http://doi.org/10.1080/09617353.2016.1148923', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-nur-yet-majumdar-usingoperationaldatafordecisionmakingafeasibilitystudyinrailmaintenance-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Marsh:2016nr,\n\tAbstract = {In many organisations, large databases are created as part of the business operation: the promise of ?big data? is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts? knowledge is vital for the model?s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.},\n\tAnnote = {doi: 10.1080/09617353.2016.1148923},\n\tAuthor = {Marsh, William and Nur, Khalid and Yet, Barbaros and Majumdar, Arnab},\n\tBooktitle = {Safety and Reliability},\n\tDate = {2016/01/02},\n\tDate-Added = {2016-10-24 22:06:10 +0000},\n\tDate-Modified = {2016-11-05 23:32:15 +0000},\n\tDoi = {10.1080/09617353.2016.1148923},\n\tIsbn = {0961-7353},\n\tJournal = {Safety and Reliability},\n\tJournal1 = {Safety and Reliability},\n\tMonth = {jan},\n\tNumber = {1},\n\tPages = {35--47},\n\tPublisher = {Taylor \\&amp; Francis},\n\tTitle = {Using operational data for decision making: a feasibility study in rail maintenance},\n\tUrl = {http://dx.doi.org/10.1080/09617353.2016.1148923},\n\tVolume = {36},\n\tYear = {2016},\n\tYear1 = {2016},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1080/09617353.2016.1148923}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  In many organisations, large databases are created as part of the business operation: the promise of ?big data? is to extract information from these databases to make smarter decisions. We explore the feasibility of this approach for better decision-making for maintenance, specifically for rail infrastructure. We argue that the data should be used within a Bayesian framework with the aim of inferring the underlying state of the system so we can predict future failures and improve decision-making. Within this framework, some data is diagnostic of this underlying state and other data have a causal influence. The framework can be realised as a Bayesian network and the probabilistic relationships in this network can be learnt from data. However, the network cannot be created just from data; instead experts? knowledge is vital for the model?s structure as some variables representing the underlying state of the system may not be present in the data. We outline an architecture for a smart decision tool and show that the GB railway industry has the data needed. The challenges of developing such a tool are also discussed. For example, the required data are distributed across multiple databases and both within and between these databases important relationships, such as physical proximity, may not be represented explicitly.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2', event);\">\n    Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Constantinou, A. C.; Yet, B.; Fenton, N.; Neil, M.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  <i>Artificial Intelligence in Medicine</i>, 66: 41–52.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2\"\n     onclick=\"log_download('constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.artmed.2015.09.002\"\n     onclick=\"log_download('constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016', 'http://doi.org/10.1016/j.artmed.2015.09.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('constantinou-yet-fenton-neil-marsh-valueofinformationanalysisforinterventionalandcounterfactualbayesiannetworksinforensicmedicalsciences-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{constantinou2015value,\n\tAbstract = {Objectives\nInspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision.\n\nMethod\nThe method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks.\n\nResults\nThe method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%).\n\nConclusions\nWe have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.},\n\tAuthor = {Constantinou, Anthony Costa and Yet, Barbaros and Fenton, Norman and Neil, Martin and Marsh, William},\n\tDate-Modified = {2016-11-05 23:30:54 +0000},\n\tDoi = {10.1016/j.artmed.2015.09.002},\n\tJournal = {Artificial Intelligence in Medicine},\n\tPages = {41--52},\n\tTitle = {Value of Information analysis for Interventional and Counterfactual Bayesian networks in Forensic Medical Sciences},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},\n\tVolume = {66},\n\tYear = {2016},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/10760/Fenton%20Value%20of%20Information%20analysis%20for%20interventional%20and%20counterfactual%20Bayesian%20networks%20in%20forensic%20medical%20sciences%202015%20Accepted.pdf?sequence=2},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2015.09.002}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Objectives Inspired by real-world examples from the forensic medical sciences domain, we seek to determine whether a decision about an interventional action could be subject to amendments on the basis of some incomplete information within the model, and whether it would be worthwhile for the decision maker to seek further information prior to suggesting a decision. Method The method is based on the underlying principle of Value of Information to enhance decision analysis in interventional and counterfactual Bayesian networks. Results The method is applied to two real-world Bayesian network models (previously developed for decision support in forensic medical sciences) to examine the average gain in terms of both Value of Information (average relative gain ranging from 11.45% and 59.91%) and decision making (potential amendments in decision making ranging from 0% to 86.8%). Conclusions We have shown how the method becomes useful for decision makers, not only when decision making is subject to amendments on the basis of some unknown risk factors, but also when it is not. Knowing that a decision outcome is independent of one or more unknown risk factors saves us from the trouble of seeking information about the particular set of risk factors. Further, we have also extended the assessment of this implication to the counterfactual case and demonstrated how answers about interventional actions are expected to change when some unknown factors become known, and how useful this becomes in forensic medical science.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016', 'https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587', event);\">\n    From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Constantinou, A. C.; Fenton, N.; Marsh, W.;  and Radlinski, L.\n</span>\n\n  \n\n</span>\n\n  <i>Artificial Intelligence in Medicine</i>, 67: 75-93.  2016.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587\"\n     onclick=\"log_download('constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016', 'https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.artmed.2016.01.002\"\n     onclick=\"log_download('constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016', 'http://doi.org/10.1016/j.artmed.2016.01.002', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('constantinou-fenton-marsh-radlinski-fromcomplexquestionnaireandinterviewingdatatointelligentbayesiannetworkmodelsformedicaldecisionsupport-2016')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{constantinou2016complex,\n\tAbstract = {OBJECTIVES: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.},\n\tAuthor = {Constantinou, Anthony Costa and Fenton, Norman and Marsh, William and Radlinski, Lukasz},\n\tDate-Modified = {2016-11-05 23:32:53 +0000},\n\tDoi = {10.1016/j.artmed.2016.01.002},\n\tJournal = {Artificial Intelligence in Medicine},\n\tPages = {75-93},\n\tTitle = {From complex questionnaire and interviewing data to intelligent Bayesian Network models for medical decision support},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},\n\tVolume = {67},\n\tYear = {2016},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/11587},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.artmed.2016.01.002}}\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  OBJECTIVES: (1) To develop a rigorous and repeatable method for building effective Bayesian network (BN) models for medical decision support from complex, unstructured and incomplete patient questionnaires and interviews that inevitably contain examples of repetitive, redundant and contradictory responses; (2) To exploit expert knowledge in the BN development since further data acquisition is usually not possible; (3) To ensure the BN model can be used for interventional analysis; (4) To demonstrate why using data alone to learn the model structure and parameters is often unsatisfactory even when extensive data is available. METHOD: The method is based on applying a range of recent BN developments targeted at helping experts build BNs given limited data. While most of the components of the method are based on established work, its novelty is that it provides a rigorous consolidated and generalised framework that addresses the whole life-cycle of BN model development. The method is based on two original and recent validated BN models in forensic psychiatry, known as DSVM-MSS and DSVM-P. RESULTS: When employed with the same datasets, the DSVM-MSS demonstrated competitive to superior predictive performance (AUC scores 0.708 and 0.797) against the state-of-the-art (AUC scores ranging from 0.527 to 0.705), and the DSVM-P demonstrated superior predictive performance (cross-validated AUC score of 0.78) against the state-of-the-art (AUC scores ranging from 0.665 to 0.717). More importantly, the resulting models go beyond improving predictive accuracy and into usefulness for risk management purposes through intervention, and enhanced decision support in terms of answering complex clinical questions that are based on unobserved evidence. CONCLUSIONS: This development process is applicable to any application domain which involves large-scale decision analysis based on such complex information, rather than based on data with hard facts, and in conjunction with the incorporation of expert knowledge for decision support via intervention. The novelty extends to challenging the decision scientists to reason about building models based on what information is really required for inference, rather than based on what data is available and hence, forces decision scientists to use available data in a much smarter way.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2015\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2015')\"\n      onkeypress=\"toggleGroup('group_2015')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2015</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015', 'https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf', event);\">\n    Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Perkins, Z. B.; Yet, B.; Glasgow, S.; Cole, E.; Marsh, W.; Brohi, K.; Rasmussen, T. E.;  and Tai, N. R. M.\n</span>\n\n  \n\n</span>\n\n  <i>British Journal of Surgery</i>, 102(5): 436–450.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf\"\n     onclick=\"log_download('perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015', 'https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1002/bjs.9689\"\n     onclick=\"log_download('perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015', 'http://doi.org/10.1002/bjs.9689', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('perkins-yet-glasgow-cole-marsh-brohi-rasmussen-tai-metaanalysisofprognosticfactorsforamputationfollowingsurgicalrepairoflowerextremityvasculartrauma-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{Perkins2015,\n\tAbstract = {\nBackground\nLower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT.\n\nMethods\nA systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor.\n\nResults\nForty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation.\n\nConclusion\nA significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling.\n},\n\tAuthor = {Perkins, Z. B. and Yet, B. and Glasgow, S. and Cole, E. and Marsh, W. and Brohi, K. and Rasmussen, T. E. and Tai, N. R. M.},\n\tDate-Added = {2016-11-05 17:30:11 +0000},\n\tDate-Modified = {2016-11-05 23:33:25 +0000},\n\tDoi = {10.1002/bjs.9689},\n\tIssn = {1365-2168},\n\tJournal = {British Journal of Surgery},\n\tNumber = {5},\n\tPages = {436--450},\n\tPublisher = {John Wiley &amp; Sons, Ltd},\n\tTitle = {Meta-analysis of prognostic factors for amputation following surgical repair of lower extremity vascular trauma},\n\tUrl = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf},\n\tVolume = {102},\n\tYear = {2015},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1002/bjs.9689},\n\tBdsk-Url-2 = {https://www.researchgate.net/profile/Zane_Perkins/publication/263088360_Prognostic_Factors_for_Amputation_Following_Surgical_Repair_of_Lower_Extremity_Vascular_Trauma_A_Systematic_Review_and_Meta-Analysis_of_Observational_Studies/links/555b777908ae8f66f3ad78b1.pdf}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Background Lower extremity vascular trauma (LEVT) is a major cause of amputation. A clear understanding of prognostic factors for amputation is important to inform surgical decision-making, patient counselling and risk stratification. The aim was to develop an understanding of prognostic factors for amputation following surgical repair of LEVT. Methods A systematic review was conducted to identify potential prognostic factors. Bayesian meta-analysis was used to calculate an absolute (pooled proportion) and relative (pooled odds ratio, OR) measure of the amputation risk for each factor. Results Forty-five studies, totalling 3187 discrete LEVT repairs, were included. The overall amputation rate was 10·0 (95 per cent credible interval 7·4 to 13·1) per cent. Significant prognostic factors for secondary amputation included: associated major soft tissue injury (26 versus 8 per cent for no soft tissue injury; OR 5·80), compartment syndrome (28 versus 6 per cent; OR 5·11), multiple arterial injuries (18 versus 9 per cent; OR 4·85), duration of ischaemia exceeding 6 h (24 versus 5 per cent; OR 4·40), associated fracture (14 versus 2 per cent; OR 4·30), mechanism of injury (blast 19 per cent, blunt 16 per cent, penetrating 5 per cent), anatomical site of injury (iliac 18 per cent, popliteal 14 per cent, tibial 10 per cent, femoral 4 per cent), age over 55 years (16 versus 9 per cent; OR 3·03) and sex (men 7 per cent versus women 8 per cent; OR 0·64). Shock and nerve or venous injuries were not significant prognostic factors for secondary amputation. Conclusion A significant proportion of patients who undergo lower extremity vascular trauma repair will require secondary amputation. This meta-analysis describes significant prognostic factors needed to inform surgical judgement, risk assessment and patient counselling. \n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2', event);\">\n    Risk assessment and risk management of violent reoffending among prisoners.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Constantinou, A. C.; Freestone, M.; Marsh, W.; Fenton, N.;  and Coid, J.\n</span>\n\n  \n\n</span>\n\n  <i>Expert Systems with Applications</i>, 42(21): 7511–7529.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2\"\n     onclick=\"log_download('constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Risk assessment and risk management of violent reoffending among prisoners [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.eswa.2015.05.025\"\n     onclick=\"log_download('constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015', 'http://doi.org/10.1016/j.eswa.2015.05.025', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('constantinou-freestone-marsh-fenton-coid-riskassessmentandriskmanagementofviolentreoffendingamongprisoners-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{constantinou2015risk,\n\tAbstract = {Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management -- Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.},\n\tAuthor = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Fenton, Norman and Coid, Jeremy},\n\tDate-Modified = {2016-11-05 23:33:14 +0000},\n\tDoi = {10.1016/j.eswa.2015.05.025},\n\tJournal = {Expert Systems with Applications},\n\tNumber = {21},\n\tPages = {7511--7529},\n\tPublisher = {Pergamon},\n\tTitle = {Risk assessment and risk management of violent reoffending among prisoners},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},\n\tVolume = {42},\n\tYear = {2015},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/15804/Constantinou%20-%202015%20-Risk%20assessment.pdf?sequence=2},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.eswa.2015.05.025}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Forensic medical practitioners and scientists have for several years sought improved decision support for determining and managing care and release of prisoners with mental health problems. Some of these prisoners can pose a serious threat of violence to society after release. It is, therefore, critical that the risk of violent reoffending is accurately measured and, more importantly, well managed with causal interventions to reduce this risk after release. The well-established predictors in this area of research are typically based on regression models or even some rule-based methods with no statistical composition, and these have proven to be unsuitable for simulating causal interventions for risk management. In collaboration with the medical practitioners of the Violence Prevention Research Unit (VPRU), Queen Mary University of London, we have developed a Bayesian network (BN) model for this purpose, which we call DSVM-P (Decision Support for Violence Management – Prisoners). The BN model captures the causal relationships between risk factors, interventions and violence and demonstrates significantly higher accuracy (cross-validated AUC score of 0.78) compared to well-established predictors (AUC scores ranging from 0.665 to 0.717) within this area of research, with respect to whether a prisoner is determined suitable for release. Even more important, however, the BN model also allows for specific risk factors to be targeted for causal intervention for risk management of future re-offending. Hence, unlike the previous predictors, this makes the model useful in terms of answering complex clinical questions that are based on unobserved evidence. Clinicians and probation officers who work in these areas would benefit from a system that takes account of these complex risk management considerations, since these decision support features are not available in the previous generation of models used by forensic psychiatrists.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015', 'https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774', event);\">\n    Causal inference for violence risk management and decision support in forensic psychiatry.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Constantinou, A. C.; Freestone, M.; Marsh, W.;  and Coid, J.\n</span>\n\n  \n\n</span>\n\n  <i>Decision Support Systems</i>, 80: 42–55.  2015.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774\"\n     onclick=\"log_download('constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015', 'https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Causal inference for violence risk management and decision support in forensic psychiatry [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.dss.2015.09.006\"\n     onclick=\"log_download('constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015', 'http://doi.org/10.1016/j.dss.2015.09.006', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('constantinou-freestone-marsh-coid-causalinferenceforviolenceriskmanagementanddecisionsupportinforensicpsychiatry-2015')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{constantinou2015causal,\n\tAbstract = {The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model&#x27;s ability in answering complex probabilistic queries for unobserved variables.},\n\tAuthor = {Constantinou, Anthony Costa and Freestone, Mark and Marsh, William and Coid, Jeremy},\n\tDate-Modified = {2016-11-05 23:33:35 +0000},\n\tDoi = {10.1016/j.dss.2015.09.006},\n\tJournal = {Decision Support Systems},\n\tPages = {42--55},\n\tPublisher = {North-Holland},\n\tTitle = {Causal inference for violence risk management and decision support in forensic psychiatry},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},\n\tVolume = {80},\n\tYear = {2015},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/handle/123456789/10774},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.dss.2015.09.006}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The purpose of medium secure services (MSS) is to provide accommodation, support, and treatment to individuals with enduring mental health problems who usually come into contact with the criminal justice system. These individuals are, therefore, believed to pose a risk of violence to themselves as well as to other individuals. Assessing and managing the risk of violence is considered to be a critical component for discharged decision making in MSS. Methods for violence risk assessment in this area of research are typically based on regression models or checklists with no statistical composition and which naturally demonstrate mediocre predictive performance and, more importantly, without providing genuine decision support. While Bayesian networks have become popular tools for decision support in the medical field over the last couple of decades, they have not been extensively studied in forensic psychiatry. In this paper, we describe a decision support system using Bayesian networks, which is mainly parameterised based on questionnaire, interviewing and clinical assessment data, for violence risk assessment and risk management in patients discharged from MSS. The results demonstrate moderate to significant improvements in forecasting capability. More importantly, we demonstrate how decision support is improved over the well-established approaches in this area of research, primarily by incorporating causal interventions and taking advantage of the model's ability in answering complex probabilistic queries for unobserved variables.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2014\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2014')\"\n      onkeypress=\"toggleGroup('group_2014')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2014</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Not just data: A method for improving prediction with knowledge.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Perkins, Z.; Fenton, N.; Tai, N.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 48: 28–37.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2013.10.012\"\n     onclick=\"log_download('yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014', 'http://doi.org/10.1016/j.jbi.2013.10.012', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-fenton-tai-marsh-notjustdataamethodforimprovingpredictionwithknowledge-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2014not,\n\tAbstract = {Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as &#x27;latent&#x27; variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.},\n\tAuthor = {Yet, Barbaros and Perkins, Zane and Fenton, Norman and Tai, Nigel and Marsh, William},\n\tDate-Modified = {2016-11-05 23:33:44 +0000},\n\tDoi = {10.1016/j.jbi.2013.10.012},\n\tJournal = {Journal of biomedical informatics},\n\tPages = {28--37},\n\tPublisher = {Academic Press},\n\tTitle = {Not just data: A method for improving prediction with knowledge},\n\tVolume = {48},\n\tYear = {2014},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2013.10.012}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many medical conditions are only indirectly observed through symptoms and tests. Developing predictive models for such conditions is challenging since they can be thought of as 'latent' variables. They are not present in the data and often get confused with measurements. As a result, building a model that fits data well is not the same as making a prediction that is useful for decision makers. In this paper, we present a methodology for developing Bayesian network (BN) models that predict and reason with latent variables, using a combination of expert knowledge and available data. The method is illustrated by a case study into the prediction of acute traumatic coagulopathy (ATC), a disorder of blood clotting that significantly increases the risk of death following traumatic injuries. There are several measurements for ATC and previous models have predicted one of these measurements instead of the state of ATC itself. Our case study illustrates the advantages of models that distinguish between an underlying latent condition and its measurements, and of a continuing dialogue between the modeller and the domain experts as the model is developed using knowledge as well as data.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yet-marsh-compatibleandincompatibleabstractionsinbayesiannetworks-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Compatible and incompatible abstractions in Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.;  and Marsh, D W. R\n</span>\n\n  \n\n</span>\n\n  <i>Knowledge-Based Systems</i>, 62: 84–97.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.knosys.2014.02.020\"\n     onclick=\"log_download('yet-marsh-compatibleandincompatibleabstractionsinbayesiannetworks-2014', 'http://doi.org/10.1016/j.knosys.2014.02.020', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-marsh-compatibleandincompatibleabstractionsinbayesiannetworks-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-marsh-compatibleandincompatibleabstractionsinbayesiannetworks-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2014compatible,\n\tAbstract = {The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts&#x27; knowledge. In this paper, we propose a method for abstracting the BN structure by using four &#x60;abstraction&#x27; operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.},\n\tAuthor = {Yet, Barbaros and Marsh, D William R},\n\tDate-Modified = {2016-11-05 23:34:05 +0000},\n\tDoi = {10.1016/j.knosys.2014.02.020},\n\tJournal = {Knowledge-Based Systems},\n\tPages = {84--97},\n\tPublisher = {Elsevier},\n\tTitle = {Compatible and incompatible abstractions in Bayesian networks},\n\tVolume = {62},\n\tYear = {2014},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.knosys.2014.02.020}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The graphical structure of a Bayesian network (BN) makes it a technology well-suited for developing decision support models from a combination of domain knowledge and data. The domain knowledge of experts is used to determine the graphical structure of the BN, corresponding to the relationships and between variables, and data is used for learning the strength of these relationships. However, the available data seldom match the variables in the structure that is elicited from experts, whose models may be quite detailed; consequently, the structure needs to be abstracted to match the data. Up to now, this abstraction has been informal, loosening the link between the final model and the experts' knowledge. In this paper, we propose a method for abstracting the BN structure by using four `abstraction' operations: node removal, node merging, state-space collapsing and edge removal. Some of these steps introduce approximations, which can be identified from changes in the set of conditional independence (CI) assertions of a network.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Combining data and meta-analysis to build Bayesian networks for clinical decision support.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Perkins, Z. B; Rasmussen, T. E; Tai, N. R.;  and Marsh, D W. R\n</span>\n\n  \n\n</span>\n\n  <i>Journal of biomedical informatics</i>, 52: 373–385.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.jbi.2014.07.018\"\n     onclick=\"log_download('yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014', 'http://doi.org/10.1016/j.jbi.2014.07.018', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-rasmussen-tai-marsh-combiningdataandmetaanalysistobuildbayesiannetworksforclinicaldecisionsupport-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2014combining,\n\tAbstract = {Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report &#x60;univariate&#x27; relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model -- ignoring some complexities of the problem -- or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.},\n\tAuthor = {Yet, Barbaros and Perkins, Zane B and Rasmussen, Todd E and Tai, Nigel RM and Marsh, D William R},\n\tDate-Modified = {2016-11-05 23:34:14 +0000},\n\tDoi = {10.1016/j.jbi.2014.07.018},\n\tJournal = {Journal of biomedical informatics},\n\tPages = {373--385},\n\tPublisher = {Academic Press},\n\tTitle = {Combining data and meta-analysis to build Bayesian networks for clinical decision support},\n\tVolume = {52},\n\tYear = {2014},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.jbi.2014.07.018}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Complex clinical decisions require the decision maker to evaluate multiple factors that may interact with each other. Many clinical studies, however, report `univariate' relations between a single factor and outcome. Such univariate statistics are often insufficient to provide useful support for complex clinical decisions even when they are pooled using meta-analysis. More useful decision support could be provided by evidence-based models that take the interaction between factors into account. In this paper, we propose a method of integrating the univariate results of a meta-analysis with a clinical dataset and expert knowledge to construct multivariate Bayesian network (BN) models. The technique reduces the size of the dataset needed to learn the parameters of a model of a given complexity. Supplementing the data with the meta-analysis results avoids the need to either simplify the model – ignoring some complexities of the problem – or to gather more data. The method is illustrated by a clinical case study into the prediction of the viability of severely injured lower extremities. The case study illustrates the advantages of integrating combined evidence into BN development: the BN developed using our method outperformed four different data-driven structure learning methods, and a well-known scoring model (MESS) in this domain.\n</div>\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"yet-perkins-tai-marsh-explicitevidenceforprognosticbayesiannetworkmodels-2014\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Explicit evidence for prognostic Bayesian network models.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Perkins, Z.; Tai, N.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  <i>Studies in Health Technology and Informatics</i>, 205: 53–7.  2014.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.3233/978-1-61499-432-9-53\"\n     onclick=\"log_download('yet-perkins-tai-marsh-explicitevidenceforprognosticbayesiannetworkmodels-2014', 'http://doi.org/10.3233/978-1-61499-432-9-53', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-tai-marsh-explicitevidenceforprognosticbayesiannetworkmodels-2014\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-tai-marsh-explicitevidenceforprognosticbayesiannetworkmodels-2014')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2014explicit,\n\tAbstract = {Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.},\n\tAuthor = {Yet, Barbaros and Perkins, Zane and Tai, Nigel and Marsh, William},\n\tDate-Modified = {2016-11-05 17:42:22 +0000},\n\tDoi = {10.3233/978-1-61499-432-9-53},\n\tJournal = {Studies in Health Technology and Informatics},\n\tPages = {53--7},\n\tTitle = {Explicit evidence for prognostic Bayesian network models},\n\tVolume = {205},\n\tYear = {2014},\n\tBdsk-File-1 = {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},\n\tBdsk-Url-1 = {https://doi.org/10.3233/978-1-61499-432-9-53}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  Many prognostic models are not adopted in clinical practice regardless of their reported accuracy. Doubts about the basis of the model is considered to be a major reason for this as the evidence behind clinical models is often not clear to anyone other than their developers. We propose a framework for representing the evidence behind Bayesian networks (BN) developed for prognostic decision support. The aim of this evidence framework is to be able to present all the evidence alongside the BN itself. We illustrate this framework by a BN developed with clinical evidence to predict coagulation disorders in trauma care.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2013\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2013')\"\n      onkeypress=\"toggleGroup('group_2013')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2013</span>\n      <span class=\"bibbase_group_count\">\n        \n        (3)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yet-bastani-raharjo-lifvergren-marsh-bergman-decisionsupportsystemforwarfarintherapymanagementusingbayesiannetworks-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Decision support system for Warfarin therapy management using Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Bastani, K.; Raharjo, H.; Lifvergren, S.; Marsh, W.;  and Bergman, B.\n</span>\n\n  \n\n</span>\n\n  <i>Decision Support Systems</i>, 55(2): 488–498.  2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.dss.2012.10.007\"\n     onclick=\"log_download('yet-bastani-raharjo-lifvergren-marsh-bergman-decisionsupportsystemforwarfarintherapymanagementusingbayesiannetworks-2013', 'http://doi.org/10.1016/j.dss.2012.10.007', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-bastani-raharjo-lifvergren-marsh-bergman-decisionsupportsystemforwarfarintherapymanagementusingbayesiannetworks-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-bastani-raharjo-lifvergren-marsh-bergman-decisionsupportsystemforwarfarintherapymanagementusingbayesiannetworks-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2013decision,\n\tAuthor = {Yet, Barbaros and Bastani, Kaveh and Raharjo, Hendry and Lifvergren, Svante and Marsh, William and Bergman, Bo},\n\tDate-Modified = {2016-11-05 23:36:58 +0000},\n\tDoi = {10.1016/j.dss.2012.10.007},\n\tJournal = {Decision Support Systems},\n\tNumber = {2},\n\tPages = {488--498},\n\tPublisher = {North-Holland},\n\tTitle = {Decision support system for Warfarin therapy management using Bayesian networks},\n\tVolume = {55},\n\tYear = {2013},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1016/j.dss.2012.10.007}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"holloway-marsh-bearfield-changeandsafetydecisionmakingfromdata-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Change and safety: decision-making from data.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Holloway, A.; Marsh, W.;  and Bearfield, G.\n</span>\n\n  \n\n</span>\n\n  <i>Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit</i>, 227(6): 704–714.  2013.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1177/0954409713498381\"\n     onclick=\"log_download('holloway-marsh-bearfield-changeandsafetydecisionmakingfromdata-2013', 'http://doi.org/10.1177/0954409713498381', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/holloway-marsh-bearfield-changeandsafetydecisionmakingfromdata-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('holloway-marsh-bearfield-changeandsafetydecisionmakingfromdata-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{holloway2013change,\n\tAuthor = {Holloway, Anna and Marsh, William and Bearfield, George},\n\tDate-Modified = {2016-11-05 23:37:33 +0000},\n\tDoi = {10.1177/0954409713498381},\n\tJournal = {Proceedings of the Institution of Mechanical Engineers, Part F: Journal of rail and rapid transit},\n\tNumber = {6},\n\tPages = {704--714},\n\tPublisher = {Sage Publications},\n\tTitle = {Change and safety: decision-making from data},\n\tVolume = {227},\n\tYear = {2013},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1177/0954409713498381}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"winther-marsh-hazardsaccidentsandeventsalandofconfusingterms-2013\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Hazards, accidents and events—a land of confusing terms.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Winther, R.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  In <i>ESREL 2013. Safety, Reliability and Risk Analysis: Beyond the Horizon</i>, pages 2545–2553,  2013. CRC Press\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1201/b15938-381\"\n     onclick=\"log_download('winther-marsh-hazardsaccidentsandeventsalandofconfusingterms-2013', 'http://doi.org/10.1201/b15938-381', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/winther-marsh-hazardsaccidentsandeventsalandofconfusingterms-2013\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n  &nbsp;\n  <span class=\"bibbase_stats_paper\" style=\"color: #777;\">\n    <span>1 download</span>\n  </span>\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('winther-marsh-hazardsaccidentsandeventsalandofconfusingterms-2013')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{winther2013hazards,\n\tAuthor = {Winther, Rune and Marsh, William},\n\tBooktitle = {ESREL 2013. Safety, Reliability and Risk Analysis: Beyond the Horizon},\n\tDate-Modified = {2016-11-05 23:36:02 +0000},\n\tDoi = {10.1201/b15938-381},\n\tOrganization = {CRC Press},\n\tPages = {2545--2553},\n\tTitle = {Hazards, accidents and events---a land of confusing terms},\n\tYear = {2013},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1201/b15938-381}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2011\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2011')\"\n      onkeypress=\"toggleGroup('group_2011')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2011</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2', event);\">\n    Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Yet, B.; Perkins, Z.; Marsh, W.;  and Fenton, N.\n</span>\n\n  \n\n</span>\n\n  <i>Probabilistic Problem Solving in BioMedicine</i>,107–120.  2011.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2\"\n     onclick=\"log_download('yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('yet-perkins-marsh-fenton-towardsamethodofbuildingcausalbayesiannetworksforprognosticdecisionsupport-2011')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{yet2011towards,\n\tAbstract = {We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model confiorms to the clinician&#x27;s way of reasoning and so that we can predict the probable effect of the available interventions. Since we cannot always depend on data from controlled trials, we depend instead on &#x27;clinical knowledge&#x27; and it is therefore vital that this elicited rigorously. We propose three stages of knowledge modeling covering the treatment process, the information generated by the process and the causal relationship. These stages lead to a causal Bayesian network, which is used to predict the patient outcome under different treatment options.},\n\tAuthor = {Yet, Barbaros and Perkins, Zane and Marsh, William and Fenton, Norman},\n\tDate-Modified = {2016-11-05 17:51:59 +0000},\n\tJournal = {Probabilistic Problem Solving in BioMedicine},\n\tPages = {107--120},\n\tTitle = {Towards a Method of Building Causal Bayesian Networks for Prognostic Decision Support},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2},\n\tYear = {2011},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/3204/MARSH.TowardsAMethod2011POST.pdf?sequence=2}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  We describe a method of building a decision support system for clinicians deciding between interventions, using Bayesian Networks (BNs). Using a case study of the amputation of traumatically injured extremities, we explain why existing prognostic models used as decision aids have not been successful in practice. A central idea is the importance of modeling causal relationships, both so that the model confiorms to the clinician's way of reasoning and so that we can predict the probable effect of the available interventions. Since we cannot always depend on data from controlled trials, we depend instead on 'clinical knowledge' and it is therefore vital that this elicited rigorously. We propose three stages of knowledge modeling covering the treatment process, the information generated by the process and the causal relationship. These stages lead to a causal Bayesian network, which is used to predict the patient outcome under different treatment options.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2010\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2010')\"\n      onkeypress=\"toggleGroup('group_2010')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2010</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2', event);\">\n    Causal Modelling of Lower Consequence Rail Safety Incidents.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bearfield, G.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  In <i>Ale, B, Papazuglo, I & Zio, E (eds) Back to the future. Proceedings of the European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, 5-9 September 2010. ISBN 0415604273</i>,  2010. CRC Press Inc\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2\"\n     onclick=\"log_download('bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010', 'https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"Causal Modelling of Lower Consequence Rail Safety Incidents [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n  &nbsp;\n  <a class=\"bibbase_abstract_link bibbase link\"\n     href=\"#\"\n     onclick=\"showAbstract(event); return false;\">\n    abstract <i class=\"fa fa-caret-down\"></i></a>\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bearfield-marsh-causalmodellingoflowerconsequencerailsafetyincidents-2010')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{bearfield2010causal,\n\tAbstract = {The Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk.},\n\tAuthor = {Bearfield, George and Marsh, William},\n\tBooktitle = {Ale, B, Papazuglo, I \\&amp; Zio, E (eds) Back to the future. Proceedings of the European Safety and Reliability Conference 2010 (ESREL 2010), Rhodes, Greece, 5-9 September 2010. ISBN 0415604273},\n\tDate-Modified = {2016-11-05 17:57:28 +0000},\n\tPublisher = {CRC Press Inc},\n\tTitle = {Causal Modelling of Lower Consequence Rail Safety Incidents},\n\tUrl = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2},\n\tYear = {2010},\n\tBdsk-Url-1 = {https://qmro.qmul.ac.uk/xmlui/bitstream/handle/123456789/1340/MARSHCausalModelling2010FINAL.pdf?sequence=2}}\n\n</pre>\n</div>\n\n\n<div class=\"well well-small bibbase\" data-type=\"abstract\"\n     style=\"display:none\">\n  The Safety Risk Model (SRM) is a key source of information for the GB rail industry. It is a structured representation of the 120 hazardous events that can lead to injury or death during the operation of the railway and is used to estimate the risk to passengers, workers and third parties. The SRM includes both rare but high consequence events such as train collisions and more frequent but lower consequence events such as passenger accidents at stations. In aggregate, these lower consequence events make an important contribution to the overall risk, which is measured by a weighted sum of injuries of different severity. Where possible, the SRM is derived from historical incident data, but the derivation of the model parameters still present challenges, which differ for different subsets of events. High consequence events occur rarely so it is necessary to use expert judgement in detailed models of these incidents. In comparison, the low consequence events occur more frequently, but both records of incidents and the models in the SRM are less detailed. The frequency of these low consequence events is sufficient to allow both the absolute risk and trends in the overall risk to be monitored directly. However, without explicit causal factors in the data or the model, the models are less able to support risk management directly, since this requires estimates of the risk reduction possible from particular interventions and control measures. Moreover, such estimates must be made locally, taking account of the local conditions, and at each location even the low consequence events are infrequent. In this paper we describe an approach to modelling the causes of low consequence events in a way that supports the management of risk. We show both how to extract more information from the available data and how to make use of expert judgement about contributory factors. Our approach uses Bayesian networks: we argue their advantages over fault and event trees for modelling incidents that have many contributory causes. Finally, we show how the new approach improves safety management, both by estimating the contribution of the underlying causes to this risk and by predicting how possible management interventions and control measures would reduce this risk.\n</div>\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2009\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2009')\"\n      onkeypress=\"toggleGroup('group_2009')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2009</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"marsh-bearfield-whyriskmodelsshouldbeparameterised-2009\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Why Risk Models should be Parameterised.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, D. W. R.;  and Bearfield, G. J.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of the Ninth International Scientific School MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg, Russia)</i>, July 2009. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-bearfield-whyriskmodelsshouldbeparameterised-2009\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-bearfield-whyriskmodelsshouldbeparameterised-2009')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Marsh2009,\n\tAuthor = {Marsh, D. William R. and Bearfield, George J.},\n\tBooktitle = {Proceedings of the Ninth International Scientific School MASR-2009 Modeling and Analysis of Safety and Risk in Complex Systems (Saint-Petersburg, Russia)},\n\tDate-Added = {2016-02-20 17:11:29 +0000},\n\tDate-Modified = {2016-02-20 17:14:07 +0000},\n\tMonth = {July},\n\tTitle = {Why Risk Models should be Parameterised.},\n\tYear = {2009}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2008\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2008')\"\n      onkeypress=\"toggleGroup('group_2008')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2008</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"marsh-bearfield-generalizingeventtreesusingbayesiannetworks-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Generalizing event trees using Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, D.;  and Bearfield, G\n</span>\n\n  \n\n</span>\n\n  <i>Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability</i>, 222(2): 105–114.  2008.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1243/1748006XJRR131\"\n     onclick=\"log_download('marsh-bearfield-generalizingeventtreesusingbayesiannetworks-2008', 'http://doi.org/10.1243/1748006XJRR131', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-bearfield-generalizingeventtreesusingbayesiannetworks-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-bearfield-generalizingeventtreesusingbayesiannetworks-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{marsh2008generalizing,\n\tAuthor = {Marsh, DWR and Bearfield, G},\n\tDate-Modified = {2016-11-06 19:55:16 +0000},\n\tDoi = {10.1243/1748006XJRR131},\n\tJournal = {Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability},\n\tNumber = {2},\n\tPages = {105--114},\n\tPublisher = {SAGE Publications},\n\tTitle = {Generalizing event trees using Bayesian networks},\n\tVolume = {222},\n\tYear = {2008},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1243/1748006XJRR131}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"fenton-neil-marsh-hearty-radliski-krause-ontheeffectivenessofearlylifecycledefectpredictionwithbayesiannets-2008\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  On the effectiveness of early life cycle defect prediction with Bayesian Nets.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fenton, N.; Neil, M.; Marsh, W.; Hearty, P.; Radliński, Ł.;  and Krause, P.\n</span>\n\n  \n\n</span>\n\n  <i>Empirical Software Engineering</i>, 13(5): 499–537.  2008.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/s10664-008-9072-x\"\n     onclick=\"log_download('fenton-neil-marsh-hearty-radliski-krause-ontheeffectivenessofearlylifecycledefectpredictionwithbayesiannets-2008', 'http://doi.org/10.1007/s10664-008-9072-x', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fenton-neil-marsh-hearty-radliski-krause-ontheeffectivenessofearlylifecycledefectpredictionwithbayesiannets-2008\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fenton-neil-marsh-hearty-radliski-krause-ontheeffectivenessofearlylifecycledefectpredictionwithbayesiannets-2008')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{fenton2008effectiveness,\n\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radli{\\&#x27;n}ski, {\\L}ukasz and Krause, Paul},\n\tDate-Modified = {2016-11-06 19:58:08 +0000},\n\tDoi = {10.1007/s10664-008-9072-x},\n\tJournal = {Empirical Software Engineering},\n\tNumber = {5},\n\tPages = {499--537},\n\tPublisher = {Springer US},\n\tTitle = {On the effectiveness of early life cycle defect prediction with Bayesian Nets},\n\tVolume = {13},\n\tYear = {2008},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1007/s10664-008-9072-x}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2007\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2007')\"\n      onkeypress=\"toggleGroup('group_2007')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2007</span>\n      <span class=\"bibbase_group_count\">\n        \n        (4)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"dray-bearfield-marsh-constructingscalableandparameterisedsystemwideriskmodels-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Constructing Scalable and Parameterised System Wide Risk Models.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Dray, P.; Bearfield, G. J.;  and Marsh, D W. R.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of 25th International System Safety Conference, Baltimore, USA</i>, August 2007. \n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/dray-bearfield-marsh-constructingscalableandparameterisedsystemwideriskmodels-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('dray-bearfield-marsh-constructingscalableandparameterisedsystemwideriskmodels-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{Dray2007,\n\tAuthor = {Dray, Peter and Bearfield, George J. and Marsh, D William R.},\n\tBooktitle = {Proceedings of 25th International System Safety Conference, Baltimore, USA},\n\tDate-Added = {2016-02-20 16:34:41 +0000},\n\tDate-Modified = {2016-11-06 19:59:37 +0000},\n\tMonth = {August},\n\tTitle = {Constructing Scalable and Parameterised System Wide Risk Models},\n\tYear = {2007}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"marsh-bearfield-representingparameterisedfaulttreesusingbayesiannetworks-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Representing parameterised fault trees using Bayesian networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, W.;  and Bearfield, G.\n</span>\n\n  \n\n</span>\n\n  In <i>SAFECOMP'07 26th international conference on Computer Safety, Reliability, and Security</i>, pages 120–133,  2007. Springer Berlin Heidelberg\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1007/978-3-540-75101-4\"\n     onclick=\"log_download('marsh-bearfield-representingparameterisedfaulttreesusingbayesiannetworks-2007', 'http://doi.org/10.1007/978-3-540-75101-4', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-bearfield-representingparameterisedfaulttreesusingbayesiannetworks-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-bearfield-representingparameterisedfaulttreesusingbayesiannetworks-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{marsh2007representing,\n\tAuthor = {Marsh, William and Bearfield, George},\n\tBooktitle = {SAFECOMP&#x27;07 26th international conference on Computer Safety, Reliability, and Security},\n\tDate-Modified = {2016-11-06 20:06:31 +0000},\n\tDoi = {10.1007/978-3-540-75101-4},\n\tOrganization = {Springer Berlin Heidelberg},\n\tPages = {120--133},\n\tTitle = {Representing parameterised fault trees using Bayesian networks},\n\tYear = {2007},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1007/978-3-540-75101-4}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"fenton-neil-marsh-hearty-radlinski-krause-projectdataincorporatingqualitativefactorsforimprovedsoftwaredefectprediction-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Project data incorporating qualitative factors for improved software defect prediction.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fenton, N.; Neil, M.; Marsh, W.; Hearty, P.; Radlinski, L.;  and Krause, P.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceeding PROMISE '07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering</i>, pages 2–11,  2007. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n  <a href=\"http://doi.org/10.1109/PROMISE.2007.11\"\n     onclick=\"log_download('fenton-neil-marsh-hearty-radlinski-krause-projectdataincorporatingqualitativefactorsforimprovedsoftwaredefectprediction-2007', 'http://doi.org/10.1109/PROMISE.2007.11', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fenton-neil-marsh-hearty-radlinski-krause-projectdataincorporatingqualitativefactorsforimprovedsoftwaredefectprediction-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fenton-neil-marsh-hearty-radlinski-krause-projectdataincorporatingqualitativefactorsforimprovedsoftwaredefectprediction-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{fenton2007project,\n\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Radlinski, Lukasz and Krause, Paul},\n\tBooktitle = {Proceeding PROMISE &#x27;07 Proceedings of the Third International Workshop on Predictor Models in Software Engineering},\n\tDate-Modified = {2016-11-06 20:24:32 +0000},\n\tDoi = {10.1109/PROMISE.2007.11},\n\tKeywords = {Software projects},\n\tOrganization = {IEEE},\n\tPages = {2--11},\n\tTitle = {Project data incorporating qualitative factors for improved software defect prediction},\n\tYear = {2007},\n\tBdsk-Url-1 = {http://dx.doi.org/10.1109/PROMISE.2007.11}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"marsh-bearfield-mergingeventtreesusingbayesiannetworks-2007\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Merging Event Trees using Bayesian Networks.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, D.;  and Bearfield, G.\n</span>\n\n  \n\n</span>\n\n  <i>Proceedings of ESREL 2007, Stavanger, Norway</i>. June 2007.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-bearfield-mergingeventtreesusingbayesiannetworks-2007\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-bearfield-mergingeventtreesusingbayesiannetworks-2007')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{marsh2007merging,\n\tAuthor = {Marsh, DWR and Bearfield, GJ},\n\tDate-Modified = {2016-02-20 17:16:42 +0000},\n\tJournal = {Proceedings of ESREL 2007, Stavanger, Norway},\n\tMonth = {June},\n\tTitle = {Merging Event Trees using Bayesian Networks},\n\tYear = {2007}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2006\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2006')\"\n      onkeypress=\"toggleGroup('group_2006')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2006</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006', 'https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf', event);\">\n    Predicting software defects in varying development lifecycles using Bayesian nets.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fenton, N.; Neil, M.; Marsh, W.; Hearty, P.; Marquez, D.; Krause, P.;  and Mishra, R.\n</span>\n\n  \n\n</span>\n\n  <i>Information and Software Technology</i>, 49(1): 32–43.  2006.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf\"\n     onclick=\"log_download('fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006', 'https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/pdf.svg\"\n\t     alt=\"Predicting software defects in varying development lifecycles using Bayesian nets [pdf]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"http://doi.org/10.1016/j.infsof.2006.09.001\"\n     onclick=\"log_download('fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006', 'http://doi.org/10.1016/j.infsof.2006.09.001', event.ctrlKey)\"\n     class=\"bibbase doi link\">\n    <span>doi</span></a>\n  &nbsp;\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fenton-neil-marsh-hearty-marquez-krause-mishra-predictingsoftwaredefectsinvaryingdevelopmentlifecyclesusingbayesiannets-2006')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{fenton2006predicting,\n\tAuthor = {Fenton, Norman and Neil, Martin and Marsh, William and Hearty, Peter and Marquez, David and Krause, Paul and Mishra, Rajat},\n\tDate-Modified = {2016-11-06 20:31:20 +0000},\n\tDoi = {10.1016/j.infsof.2006.09.001},\n\tJournal = {Information and Software Technology},\n\tNumber = {1},\n\tPages = {32--43},\n\tPublisher = {Elsevier},\n\tTitle = {Predicting software defects in varying development lifecycles using Bayesian nets},\n\tUrl = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},\n\tVolume = {49},\n\tYear = {2006},\n\tBdsk-Url-1 = {https://www.eecs.qmul.ac.uk/~norman/papers/ist_fenton.pdf},\n\tBdsk-Url-2 = {http://dx.doi.org/10.1016/j.infsof.2006.09.001}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2005\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2005')\"\n      onkeypress=\"toggleGroup('group_2005')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2005</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"bearfield-marsh-generalisingeventtreesusingbayesiannetworkswithacasestudyoftrainderailment-2005\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Generalising event trees using Bayesian networks with a case study of train derailment.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Bearfield, G.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  In <i>Proceedings of 24th International Conference, SAFECOMP 2005, Computer Safety, Reliability, and Security</i>, volume 3688, of Lecture Notes in Computer Science, pages 52–66. Springer Berlin Heidelberg,  2005.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/bearfield-marsh-generalisingeventtreesusingbayesiannetworkswithacasestudyoftrainderailment-2005\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('bearfield-marsh-generalisingeventtreesusingbayesiannetworkswithacasestudyoftrainderailment-2005')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{bearfield2005generalising,\n\tAuthor = {Bearfield, George and Marsh, William},\n\tBooktitle = {Proceedings of 24th International Conference, SAFECOMP 2005, Computer Safety, Reliability, and Security},\n\tDate-Modified = {2016-02-20 17:44:15 +0000},\n\tPages = {52--66},\n\tPublisher = {Springer Berlin Heidelberg},\n\tSeries = {Lecture Notes in Computer Science},\n\tTitle = {Generalising event trees using Bayesian networks with a case study of train derailment},\n\tVolume = {3688},\n\tYear = {2005}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_2004\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_2004')\"\n      onkeypress=\"toggleGroup('group_2004')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>2004</span>\n      <span class=\"bibbase_group_count\">\n        \n        (2)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"marsh-bearfield-usingbayesiannetworkstomodelaccidentcausationintheukrailwayindustry-2004\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Using Bayesian networks to model accident causation in the UK railway industry.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, W.;  and Bearfield, G.\n</span>\n\n  \n\n</span>\n\n  In <i>Probabilistic Safety Assessment and Management</i>, pages 3597–3602. Springer London,  2004.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-bearfield-usingbayesiannetworkstomodelaccidentcausationintheukrailwayindustry-2004\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-bearfield-usingbayesiannetworkstomodelaccidentcausationintheukrailwayindustry-2004')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@incollection{marsh2004using,\n\tAuthor = {Marsh, William and Bearfield, George},\n\tBooktitle = {Probabilistic Safety Assessment and Management},\n\tPages = {3597--3602},\n\tPublisher = {Springer London},\n\tTitle = {Using Bayesian networks to model accident causation in the UK railway industry},\n\tYear = {2004}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n  <div class=\"bibbase_paper\" id=\"fenton-marsh-neil-cates-forey-tailor-makingresourcedecisionsforsoftwareprojects-2004\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Making resource decisions for software projects.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Fenton, N.; Marsh, W.; Neil, M.; Cates, P.; Forey, S.;  and Tailor, M.\n</span>\n\n  \n\n</span>\n\n  In <i>Software Engineering, 2004. ICSE 2004. Proceedings. 26th International Conference on</i>, pages 397–406,  2004. IEEE\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/fenton-marsh-neil-cates-forey-tailor-makingresourcedecisionsforsoftwareprojects-2004\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('fenton-marsh-neil-cates-forey-tailor-makingresourcedecisionsforsoftwareprojects-2004')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{fenton2004making,\n\tAuthor = {Fenton, Norman and Marsh, William and Neil, Martin and Cates, Patrick and Forey, Simon and Tailor, Manesh},\n\tBooktitle = {Software Engineering, 2004. ICSE 2004. Proceedings. 26th International Conference on},\n\tOrganization = {IEEE},\n\tPages = {397--406},\n\tTitle = {Making resource decisions for software projects},\n\tYear = {2004}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1997\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1997')\"\n      onkeypress=\"toggleGroup('group_1997')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1997</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"marsh-harmonisationofdefencestandardsforsafetycriticalsoftware-1997\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Harmonisation of defence standards for safety-critical software.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Marsh, W\n</span>\n\n  \n\n</span>\n\n  <i>Microprocessors and Microsystems</i>, 21(1): 41–47.  1997.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/marsh-harmonisationofdefencestandardsforsafetycriticalsoftware-1997\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('marsh-harmonisationofdefencestandardsforsafetycriticalsoftware-1997')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{marsh1997harmonisation,\n\tAuthor = {Marsh, W},\n\tJournal = {Microprocessors and Microsystems},\n\tNumber = {1},\n\tPages = {41--47},\n\tPublisher = {Elsevier},\n\tTitle = {Harmonisation of defence standards for safety-critical software},\n\tVolume = {21},\n\tYear = {1997}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1995\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1995')\"\n      onkeypress=\"toggleGroup('group_1995')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1995</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"wichmann-canning-clutterbuck-winsborrow-ward-marsh-industrialperspectiveonstaticanalysis-1995\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  Industrial perspective on static analysis.\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Wichmann, B.; Canning, A.; Clutterbuck, D.; Winsborrow, L.; Ward, N.;  and Marsh, D.\n</span>\n\n  \n\n</span>\n\n  <i>Software Engineering Journal</i>, 10(2): 69.  1995.\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/wichmann-canning-clutterbuck-winsborrow-ward-marsh-industrialperspectiveonstaticanalysis-1995\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('wichmann-canning-clutterbuck-winsborrow-ward-marsh-industrialperspectiveonstaticanalysis-1995')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@article{wichmann1995industrial,\n\tAuthor = {Wichmann, BA and Canning, AA and Clutterbuck, DL and Winsborrow, LA and Ward, NJ and Marsh, DWR},\n\tJournal = {Software Engineering Journal},\n\tNumber = {2},\n\tPages = {69},\n\tPublisher = {London: published by the Institution of Electrical Engineers for the British Computer Society and the Institution of Electrical Engineers, c1986-c1996.},\n\tTitle = {Industrial perspective on static analysis},\n\tVolume = {10},\n\tYear = {1995}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n  <div class=\"bibbase_group_whole\" id=\"group_1992\">\n    <div class=\"bibbase_group\"\n      onclick=\"toggleGroup('group_1992')\"\n      onkeypress=\"toggleGroup('group_1992')\"\n      tabindex=\"0\">\n      <i class=\"fa fa-angle-down bibbase_group_head_icon\"></i>&nbsp;\n      <span>1992</span>\n      <span class=\"bibbase_group_count\">\n        \n        (1)\n        \n      </span>\n    </div>\n    <div class=\"bibbase_group_body\">\n      \n  \n  <div class=\"bibbase_paper\" id=\"carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992\">\n  <span class=\"bibbase_paper_titleauthoryear\">\n\n  \n  <span class=\"bibbase_paper_title\">\n  \n  \n  \n  <a href=\"https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK—a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false\"\n     onclick=\"return trackOutboundLink('publications', 'click', 'carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992', 'https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK—a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false', event);\">\n    SPARK—a safetyrelated Ada subset.\n  </a>\n  \n  \n  \n</span>\n\n  <span class=\"bibbase_paper_author\">\n  Carre, B.; Garnsworthy, J.;  and Marsh, W.\n</span>\n\n  \n\n</span>\n\n  In Taylor, W J, editor(s), <i>Ada in transition: Proceedings of the 1992 Ada UK International Conference</i>, of <i>Studies in computer and communications systems</i>, pages 31–45, Oct 1992. IOS Press\n  <span class=\"note\"></span>\n\n<span class=\"bibbase_note\"></span>\n\n<br class=\"bibbase_paper_content\"/>\n\n<span class=\"bibbase_paper_content dontprint\">\n\n  \n  <a href=\"https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK—a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false\"\n     onclick=\"log_download('carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992', 'https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK—a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false', event.ctrlKey, event)\">\n    <img src=\"//bibbase.org/img/filetypes/link.svg\"\n\t     alt=\"SPARK—a safetyrelated Ada subset [link]\"\n       title=\"Paper\"\n\t     class=\"bibbase_icon\"\n         ><span class=\"bibbase_icon_text\">Paper</span></a>\n  &nbsp;\n  \n\n  \n\n  \n  <a href=\"//bibbase.org/network/publication/carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992\"\n     class=\"bibbase bibbase_page link\">link</a>\n  &nbsp;\n  \n\n  <a href=\"#\"\n     onclick=\"showBib(event); return false;\"\n     class=\"bibbase bibtex link\">bibtex\n    <i class=\"fa fa-caret-down\"></i></a>\n\n  \n\n  \n\n  <!-- <a class=\"bibbase read link hidden\" -->\n  <!--    href=\"javascript:toggleRead('carre-garnsworthy-marsh-sparkasafetyrelatedadasubset-1992')\" -->\n  <!--    title=\"Marking this paper as read adds it to your library on BibBase.\" -->\n  <!--    > -->\n  <!--   <i class=\"fa fa-check\"></i>&nbsp; -->\n  <!--   <span>mark as read</span> -->\n  <!-- </a> -->\n\n  &nbsp;\n  \n  \n\n</span>\n\n<div class=\"well well-small bibbase\" data-type=\"bibtex\"\n     style=\"display:none\">\n  <pre style=\"white-space: pre-wrap;\">@inproceedings{carre1992spark,\n\tAuthor = {Carre, Bernard and Garnsworthy, Jon and Marsh, William},\n\tBooktitle = {Ada in transition: Proceedings of the 1992 Ada UK International Conference},\n\tDate-Modified = {2016-11-06 22:36:19 +0000},\n\tEditor = {Taylor, W J},\n\tMonth = {Oct},\n\tNumber = {4},\n\tPages = {31--45},\n\tPublisher = {IOS Press},\n\tSeries = {Studies in computer and communications systems},\n\tTitle = {SPARK---a safetyrelated Ada subset},\n\tUrl = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK---a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false},\n\tYear = {1992},\n\tBdsk-Url-1 = {https://books.google.co.uk/books?id=S8PKaSKkZFAC&amp;lpg=PA31&amp;ots=oS691rW-Vy&amp;dq=SPARK---a%20safety%20related%20Ada%20subset.&amp;pg=PA31#v=onepage&amp;q&amp;f=false}}\n\n</pre>\n</div>\n\n\n\n</div>\n\n\n\n\n\n    </div>\n  </div>\n\n\n\n\n  </div>\n\n\n  <!-- Modal -->\n\n  <!-- <iframe id=\"bibbase_network_frame\" -->\n  <!--         src=\"//bibbase.org/network/control\" -->\n  <!--         style=\"display: none;\"> -->\n  <!-- </iframe> -->\n\n</div>\n"}; document.write(bibbase_data.data);