Les «ADE Scorecards»: Un outil de détection par data mining des effets indésirables liés aux médicaments dans les dossiers médicaux (projet PSIP). Chazard, E., Baceanu, A., Ficheur, G., Marcilly, R., & Beuscart, R. In Staccini, P. P. M., Harmel, D. A., Darmoni, P. S. J., & Gouider, P. R., editors, Systèmes d’information pour l’amélioration de la qualité en santé, of Informatique et Santé, pages 177–188. Springer Paris, 2011. Paper doi abstract bibtex The automated detection of Adverse Drug Events (ADE) is an important issue in medical informatics. The objective of this work is to automatically detect ADEs and to present the results to physicians. 90,000 stays are extracted from the EHR of 5 French and Danish hospitals and loaded into a common repository, using a common data model. Then data mining procedures such as decision trees are used in order to get ADE detection rules that are filtered and validated by an expert committee. The procedure enables to produce 236 ADE detection rules that are able to detect 27 different kinds of outcomes. Contextualized statistics are computed for every rule in every medical department separately. The physicians of the medical departments are provided with that information by means of a web-based tool named “ADE Scorecards”. The tool is presented in the article through a use case and several screenshots. Based on a list of rules and a repository of stays, it allows for displaying the important rules, the related statistics, and the complete information about the suspicious cases. The knowledge is contextualized, i.e. it depends on the medical department. The tool is deployed in a French hospital and is currently being evaluated through a prospective impact assessment.
@incollection{chazard_ade_2011-1,
series = {Informatique et {Santé}},
title = {Les «{ADE} {Scorecards}»: {Un} outil de détection par data mining des effets indésirables liés aux médicaments dans les dossiers médicaux (projet {PSIP})},
copyright = {©2012 Springer-Verlag France S.A.R.L.},
isbn = {978-2-8178-0284-8 978-2-8178-0285-5},
shorttitle = {Les «{ADE} {Scorecards}»},
url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2011_jfim_adedetection_fr.pdf},
abstract = {The automated detection of Adverse Drug Events (ADE) is an important issue in medical informatics. The objective of this work is to automatically detect ADEs and to present the results to physicians. 90,000 stays are extracted from the EHR of 5 French and Danish hospitals and loaded into a common repository, using a common data model. Then data mining procedures such as decision trees are used in order to get ADE detection rules that are filtered and validated by an expert committee. The procedure enables to produce 236 ADE detection rules that are able to detect 27 different kinds of outcomes. Contextualized statistics are computed for every rule in every medical department separately. The physicians of the medical departments are provided with that information by means of a web-based tool named “ADE Scorecards”. The tool is presented in the article through a use case and several screenshots. Based on a list of rules and a repository of stays, it allows for displaying the important rules, the related statistics, and the complete information about the suspicious cases. The knowledge is contextualized, i.e. it depends on the medical department. The tool is deployed in a French hospital and is currently being evaluated through a prospective impact assessment.},
language = {fr},
number = {1},
urldate = {2016-01-24},
booktitle = {Systèmes d’information pour l’amélioration de la qualité en santé},
publisher = {Springer Paris},
author = {Chazard, Emmanuel and Baceanu, Adrian and Ficheur, Grégoire and Marcilly, Romaric and Beuscart, Régis},
editor = {Staccini, Pr Pascal M. and Harmel, Dr Ali and Darmoni, Pr Stéfan J. and Gouider, Pr Riadh},
year = {2011},
doi = {10.1007/978-2-8178-0285-5_16},
keywords = {Adverse drug events, Biomedicine general, Computer Appl. in Administrative Data Processing, Data Mining, Database Management, Decision Trees, Electronic Health Records, Health Informatics, Information Systems and Communication Service, Management of Computing and Information Systems},
pages = {177--188},
}
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