@article{lamer_methodology_2016,
	title = {Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: {Proposal} for an adaptable algorithm},
	volume = {129},
	issn = {1872-7565},
	shorttitle = {Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series},
	doi = {10.1016/j.cmpb.2016.01.004},
	abstract = {Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.},
	language = {eng},
	journal = {Computer Methods and Programs in Biomedicine},
	author = {Lamer, Antoine and Jeanne, Mathieu and Marcilly, Romaric and Kipnis, Eric and Schiro, Jessica and Logier, Régis and Tavernier, Benoît},
	month = jun,
	year = {2016},
	pmid = {26817405},
	keywords = {Algorithm, Anesthesia, Medical Informatics, Medical informatics, Time-series data analysis},
	pages = {160--171},
}

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When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.","language":"eng","journal":"Computer Methods and Programs in Biomedicine","author":[{"propositions":[],"lastnames":["Lamer"],"firstnames":["Antoine"],"suffixes":[]},{"propositions":[],"lastnames":["Jeanne"],"firstnames":["Mathieu"],"suffixes":[]},{"propositions":[],"lastnames":["Marcilly"],"firstnames":["Romaric"],"suffixes":[]},{"propositions":[],"lastnames":["Kipnis"],"firstnames":["Eric"],"suffixes":[]},{"propositions":[],"lastnames":["Schiro"],"firstnames":["Jessica"],"suffixes":[]},{"propositions":[],"lastnames":["Logier"],"firstnames":["Régis"],"suffixes":[]},{"propositions":[],"lastnames":["Tavernier"],"firstnames":["Benoît"],"suffixes":[]}],"month":"June","year":"2016","pmid":"26817405","keywords":"Algorithm, Anesthesia, Medical Informatics, Medical informatics, Time-series data analysis","pages":"160–171","bibtex":"@article{lamer_methodology_2016,\n\ttitle = {Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: {Proposal} for an adaptable algorithm},\n\tvolume = {129},\n\tissn = {1872-7565},\n\tshorttitle = {Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series},\n\tdoi = {10.1016/j.cmpb.2016.01.004},\n\tabstract = {Abnormal values of vital parameters such as hypotension or tachycardia may occur during anesthesia and may be detected by analyzing time-series data collected during the procedure by the Anesthesia Information Management System. When crossed with other data from the Hospital Information System, abnormal values of vital parameters have been linked with postoperative morbidity and mortality. However, methods for the automatic detection of these events are poorly documented in the literature and differ between studies, making it difficult to reproduce results. In this paper, we propose a methodology for the automatic detection of abnormal values of vital parameters. This methodology uses an algorithm allowing the configuration of threshold values for any vital parameters as well as the management of missing data. Four examples illustrate the application of the algorithm, after which it is applied to three vital signs (heart rate, SpO2, and mean arterial pressure) to all 2014 anesthetic records at our institution.},\n\tlanguage = {eng},\n\tjournal = {Computer Methods and Programs in Biomedicine},\n\tauthor = {Lamer, Antoine and Jeanne, Mathieu and Marcilly, Romaric and Kipnis, Eric and Schiro, Jessica and Logier, Régis and Tavernier, Benoît},\n\tmonth = jun,\n\tyear = {2016},\n\tpmid = {26817405},\n\tkeywords = {Algorithm, Anesthesia, Medical Informatics, Medical informatics, Time-series data analysis},\n\tpages = {160--171},\n}\n\n","author_short":["Lamer, A.","Jeanne, M.","Marcilly, R.","Kipnis, E.","Schiro, J.","Logier, R.","Tavernier, B."],"key":"lamer_methodology_2016","id":"lamer_methodology_2016","bibbaseid":"lamer-jeanne-marcilly-kipnis-schiro-logier-tavernier-methodologytoautomaticallydetectabnormalvaluesofvitalparametersinanesthesiatimeseriesproposalforanadaptablealgorithm-2016","role":"author","urls":{},"keyword":["Algorithm","Anesthesia","Medical Informatics","Medical informatics","Time-series data analysis"],"metadata":{"authorlinks":{}}},"bibtype":"article","biburl":"https://api.zotero.org/users/1729868/collections/J5D9IHP7/items?key=n4LeME0A45w9AHGzM16k3I3O&format=bibtex&limit=100","dataSources":["9jtzjDGq8xfn9xiA9","EvCdEsC2AZvZsEKth","gPTiaacnxqbN8dgMR"],"keywords":["algorithm","anesthesia","medical informatics","medical informatics","time-series data analysis"],"search_terms":["methodology","automatically","detect","abnormal","values","vital","parameters","anesthesia","time","series","proposal","adaptable","algorithm","lamer","jeanne","marcilly","kipnis","schiro","logier","tavernier"],"title":"Methodology to automatically detect abnormal values of vital parameters in anesthesia time-series: Proposal for an adaptable algorithm","year":2016}