@article{chazard_ade_2011,
	title = {The {ADE} scorecards: a tool for adverse drug event detection in electronic health records},
	volume = {166},
	copyright = {All rights reserved},
	issn = {0926-9630},
	shorttitle = {The {ADE} scorecards},
	url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2011_psip_scorecards.pdf},
	abstract = {Although several methods exist for Adverse Drug events (ADE) detection due to past hospitalizations, a tool that could display those ADEs to the physicians does not exist yet. This article presents the ADE Scorecards, a Web tool that enables to screen past hospitalizations extracted from Electronic Health Records (EHR), using a set of ADE detection rules, presently rules discovered by data mining. The tool enables the physicians to (1) get contextualized statistics about the ADEs that happen in their medical department, (2) see the rules that are useful in their department, i.e. the rules that could have enabled to prevent those ADEs and (3) review in detail the ADE cases, through a comprehensive interface displaying the diagnoses, procedures, lab results, administered drugs and anonymized records. The article shows a demonstration of the tool through a use case.},
	language = {eng},
	journal = {Studies in health technology and informatics},
	author = {Chazard, Emmanuel and Băceanu, Adrian and Ferret, Laurie and Ficheur, Grégoire},
	year = {2011},
	pmid = {21685622},
	keywords = {Data Mining, Drug Toxicity, Humans, Information Systems, Internet, Medical Records Systems, Computerized},
	pages = {169--179},
}

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