Detecting adverse drug events in discharge summaries using variations on the simple Bayes model.
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.
Visweswaran
Shyam
S
Center for Biomedical Informatics, University of Pittsburgh, Pennsylvania, USA.
Hanbury
Paul
P
Saul
Melissa
M
Cooper
Gregory F
GF
D000043:Abstracting and Indexing; D016907:Adverse Drug Reaction Reporting Systems; D000465:Algorithms; D001499:Bayes Theorem; D064420:Drug-Related Side Effects and Adverse Reactions; D006751:Hospital Information Systems; D006801:Humans; D038801:International Classification of Diseases; D016347:Medical Records Systems, Computerized / Q000145:classification; D008508:Medication Errors / Q000145:classification / Q000517:prevention & control; D010351:Patient Discharge; D012372:ROC Curve; D017432:Unified Medical Language System