Detecting adverse drug events in discharge summaries using variations on the simple Bayes model

Visweswaran, S, Hanbury, P, Saul, M, Cooper, GF. Detecting adverse drug events in discharge summaries using variations on the simple Bayes model. In: Proceedings of the Fall Symposium of the American Medical Informatics Association (Nov 2003) 689-93. PMID: 14728261 PMCID: PMC1479984

Detection and prevention of adverse events and, in particular, adverse drug events (ADEs), is an important problem in health care today. We describe the implementation and evaluation of four variations on the simple Bayes model for identifying ADE-related discharge summaries. Our results show that these probabilistic techniques achieve an ROC curve area of up to 0.77 in correctly determining which patient cases should be assigned an ADE-related ICD-9-CM code. These results suggest a potential for these techniques to contribute to the development of an automated system that helps identify ADEs, as a step toward further understanding and preventing them.

Publication Year: 
2003
Publication Credits: 
Paul Hanbury, Melissa Saul, Gregory F. Cooper
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