@article{chazard_detection_2009,
	title = {Detection of adverse drug events: proposal of a data model},
	volume = {148},
	copyright = {All rights reserved},
	issn = {0926-9630},
	shorttitle = {Detection of adverse drug events},
	url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2009_psip_datamodel.pdf},
	abstract = {Our main objective is to detect adverse drug events (ADEs) in former hospital stays. As ADEs are rare, that supposes to screen thousands of electronic health records (EHRs). For that purpose, we need to define a data model that has two main objectives: (1) being able to describe hospital stays from various hospitals (2) being tuned so as to prepare the data mining process: as ADEs are not flagged in the datasets, the data model must be optimized for ADE detection. The article presents the phases of the design and the data model that results from this work. It is compatible with many hospitals. It deals with diagnoses, drug prescriptions, lab results and administrative information. It allows for data mining and ADE detection in EHRs.},
	language = {eng},
	journal = {Studies in health technology and informatics},
	author = {Chazard, Emmanuel and Merlin, Béatrice and Ficheur, Grégoire and Sarfati, Jean-Charles and {PSIP Consortium} and Beuscart, Régis},
	year = {2009},
	pmid = {19745236},
	keywords = {Data Mining, Decision Support Techniques, Drug Toxicity, Electronic Health Records, Humans},
	pages = {63--74},
}

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