@article{chazard_data-mining-based_2009,
	title = {Data-mining-based detection of adverse drug events},
	volume = {150},
	copyright = {All rights reserved},
	issn = {0926-9630},
	url = {http://www.chazard.org/emmanuel/pdf_articles/paper_2009_mie_dataminingade.pdf},
	abstract = {Every year adverse drug events (ADEs) are known to be responsible for 98,000 deaths in the USA. Classical methods rely on report statements, expert knowledge, and staff operated record review. One of our objectives, in the PSIP project framework, is to use data mining (e.g., decision trees) to electronically identify situations leading to risk of ADEs. 10,500 hospitalization records from Denmark and France were used. 500 rules were automatically obtained, which are currently being validated by experts. A decision support system to prevent ADEs is then to be developed. The article examines a decision tree and the rules in the field of vitamin K antagonists.},
	language = {eng},
	journal = {Studies in health technology and informatics},
	author = {Chazard, Emmanuel and Preda, Cristian and Merlin, Béatrice and Ficheur, Grégoire and {PSIP consortium} and Beuscart, Régis},
	year = {2009},
	pmid = {19745372},
	keywords = {Anticoagulants, Databases, Factual, Decision Trees, Drug Toxicity, Information Storage and Retrieval, Medical Informatics, Vitamin K},
	pages = {552--556},
}

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