@InProceedings{KR4HC,
  author =       "Veruska Zamborlini and Qing Hu and Zhisheng Huang and 
                  Marcos da Silveira and Cedric Pruski and 
                  Annette ten Teije and Frank van Harmelen",
  title =        "Knowledge-driven Paper Retrieval to support updating 
                  of Clinical Guidelines: A use case on PubMed", 
  booktitle =    "Proceedings of the workshop on Knowledge Representation for 
                  Healthcare",
  editor =       "Richard Lenz and Manfred Reichert and David Riano",
  publisher =    "Springer Verlag",
  series    =    "LNCS",
  year =         2016,
  keywords = {Medical Knowledge Representation},
  urlPaper = "http://www.cs.vu.nl/~frankh/postscript/KR4HC2016.pdf",
  abstract = "Clinical Guidelines are important knowledge resources 
for medical decision making. They provide clinical recommendations based
on a collection of research findings with respect to a specific disease.
Since, new findings are regularly published, CGs are also expected to be
regularly updated. However, selecting and analysing medical publications
require a huge human efforts, even when these publications are mostly
regrouped and into repositories (e.g., MEDLINE database) and acces-
sible via a search engine (e.g PubMed). Automatically detecting those
research findings from a medical search engine such as PubMed supports
the guideline updating process. A simple search method is to select the
medical terms that appear in the conclusions of the guideline to gener-
ate a query to search for new evidences. However, some challenges rise in
this method: how to select the important terms, besides how to consider
background knowledge that may be missing or not explicitly stated in
those conclusions. In this paper we apply a knowledge model that for-
mally describes elements such as actions and their effects to investigate
(i) if it favors selecting the medical terms to compose queries and (ii) if
a search enhanced with background knowledge can provide better result
than other methods. This work explores a knowledge-driven approach
for detecting new evidences relevant for the clinical guideline update
process. Based on the outcomes of two experiments, we found that this
approach can improve the recall by retrieving more relevant evidences
than previous methods."
}

{"_id":"bNgqazT6YQ4AwQfcn","bibbaseid":"zamborlini-hu-huang-dasilveira-pruski-tenteije-vanharmelen-knowledgedrivenpaperretrievaltosupportupdatingofclinicalguidelinesausecaseonpubmed-2016","author_short":["Zamborlini, V.","Hu, Q.","Huang, Z.","da Silveira, M.","Pruski, C.","ten Teije, A.","van Harmelen, F."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["Veruska"],"propositions":[],"lastnames":["Zamborlini"],"suffixes":[]},{"firstnames":["Qing"],"propositions":[],"lastnames":["Hu"],"suffixes":[]},{"firstnames":["Zhisheng"],"propositions":[],"lastnames":["Huang"],"suffixes":[]},{"firstnames":["Marcos"],"propositions":["da"],"lastnames":["Silveira"],"suffixes":[]},{"firstnames":["Cedric"],"propositions":[],"lastnames":["Pruski"],"suffixes":[]},{"firstnames":["Annette"],"propositions":["ten"],"lastnames":["Teije"],"suffixes":[]},{"firstnames":["Frank"],"propositions":["van"],"lastnames":["Harmelen"],"suffixes":[]}],"title":"Knowledge-driven Paper Retrieval to support updating of Clinical Guidelines: A use case on PubMed","booktitle":"Proceedings of the workshop on Knowledge Representation for Healthcare","editor":[{"firstnames":["Richard"],"propositions":[],"lastnames":["Lenz"],"suffixes":[]},{"firstnames":["Manfred"],"propositions":[],"lastnames":["Reichert"],"suffixes":[]},{"firstnames":["David"],"propositions":[],"lastnames":["Riano"],"suffixes":[]}],"publisher":"Springer Verlag","series":"LNCS","year":"2016","keywords":"Medical Knowledge Representation","urlpaper":"http://www.cs.vu.nl/~frankh/postscript/KR4HC2016.pdf","abstract":"Clinical Guidelines are important knowledge resources for medical decision making. They provide clinical recommendations based on a collection of research findings with respect to a specific disease. Since, new findings are regularly published, CGs are also expected to be regularly updated. However, selecting and analysing medical publications require a huge human efforts, even when these publications are mostly regrouped and into repositories (e.g., MEDLINE database) and acces- sible via a search engine (e.g PubMed). Automatically detecting those research findings from a medical search engine such as PubMed supports the guideline updating process. A simple search method is to select the medical terms that appear in the conclusions of the guideline to gener- ate a query to search for new evidences. However, some challenges rise in this method: how to select the important terms, besides how to consider background knowledge that may be missing or not explicitly stated in those conclusions. In this paper we apply a knowledge model that for- mally describes elements such as actions and their effects to investigate (i) if it favors selecting the medical terms to compose queries and (ii) if a search enhanced with background knowledge can provide better result than other methods. This work explores a knowledge-driven approach for detecting new evidences relevant for the clinical guideline update process. Based on the outcomes of two experiments, we found that this approach can improve the recall by retrieving more relevant evidences than previous methods.","bibtex":"@InProceedings{KR4HC,\r\n author = \"Veruska Zamborlini and Qing Hu and Zhisheng Huang and \r\n Marcos da Silveira and Cedric Pruski and \r\n Annette ten Teije and Frank van Harmelen\",\r\n title = \"Knowledge-driven Paper Retrieval to support updating \r\n of Clinical Guidelines: A use case on PubMed\", \r\n booktitle = \"Proceedings of the workshop on Knowledge Representation for \r\n Healthcare\",\r\n editor = \"Richard Lenz and Manfred Reichert and David Riano\",\r\n publisher = \"Springer Verlag\",\r\n series = \"LNCS\",\r\n year = 2016,\r\n keywords = {Medical Knowledge Representation},\r\n urlPaper = \"http://www.cs.vu.nl/~frankh/postscript/KR4HC2016.pdf\",\r\n abstract = \"Clinical Guidelines are important knowledge resources \r\nfor medical decision making. They provide clinical recommendations based\r\non a collection of research findings with respect to a specific disease.\r\nSince, new findings are regularly published, CGs are also expected to be\r\nregularly updated. However, selecting and analysing medical publications\r\nrequire a huge human efforts, even when these publications are mostly\r\nregrouped and into repositories (e.g., MEDLINE database) and acces-\r\nsible via a search engine (e.g PubMed). Automatically detecting those\r\nresearch findings from a medical search engine such as PubMed supports\r\nthe guideline updating process. A simple search method is to select the\r\nmedical terms that appear in the conclusions of the guideline to gener-\r\nate a query to search for new evidences. However, some challenges rise in\r\nthis method: how to select the important terms, besides how to consider\r\nbackground knowledge that may be missing or not explicitly stated in\r\nthose conclusions. In this paper we apply a knowledge model that for-\r\nmally describes elements such as actions and their effects to investigate\r\n(i) if it favors selecting the medical terms to compose queries and (ii) if\r\na search enhanced with background knowledge can provide better result\r\nthan other methods. This work explores a knowledge-driven approach\r\nfor detecting new evidences relevant for the clinical guideline update\r\nprocess. Based on the outcomes of two experiments, we found that this\r\napproach can improve the recall by retrieving more relevant evidences\r\nthan previous methods.\"\r\n}\r\n\r\n\r\n","author_short":["Zamborlini, V.","Hu, Q.","Huang, Z.","da Silveira, M.","Pruski, C.","ten Teije, A.","van Harmelen, F."],"editor_short":["Lenz, R.","Reichert, M.","Riano, D."],"key":"KR4HC","id":"KR4HC","bibbaseid":"zamborlini-hu-huang-dasilveira-pruski-tenteije-vanharmelen-knowledgedrivenpaperretrievaltosupportupdatingofclinicalguidelinesausecaseonpubmed-2016","role":"author","urls":{"Paper":"http://www.cs.vu.nl/~frankh/postscript/KR4HC2016.pdf"},"keyword":["Medical Knowledge Representation"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"inproceedings","biburl":"http://www.cs.vu.nl/~frankh/abstracts/all.bib","dataSources":["9CnmDh6oPMNTwHksm"],"keywords":["medical knowledge representation"],"search_terms":["knowledge","driven","paper","retrieval","support","updating","clinical","guidelines","use","case","pubmed","zamborlini","hu","huang","da silveira","pruski","ten teije","van harmelen"],"title":"Knowledge-driven Paper Retrieval to support updating of Clinical Guidelines: A use case on PubMed","year":2016}