Journal
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Volume 18, Issue -, Pages I73-I80Publisher
OXFORD UNIV PRESS
DOI: 10.1136/amiajnl-2011-000417
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Funding
- National Library of Medicine [R01 LM010016, R01 LM010016-0S1, R01 LM010016-0S2, R01 LM008635, 1R01LM010140-01]
- National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) of the National Institutes of Health
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Background Adverse drug events (ADE) cause considerable harm to patients, and consequently their detection is critical for patient safety. The US Food and Drug Administration maintains an adverse event reporting system (AERS) to facilitate the detection of ADE in drugs. Various data mining approaches have been developed that use AERS to detect signals identifying associations between drugs and ADE. The signals must then be monitored further by domain experts, which is a time-consuming task. Objective To develop a new methodology that combines existing data mining algorithms with chemical information by analysis of molecular fingerprints to enhance initial ADE signals generated from AERS, and to provide a decision support mechanism to facilitate the identification of novel adverse events. Results The method achieved a significant improvement in precision in identifying known ADE, and a more than twofold signal enhancement when applied to the ADE rhabdomyolysis. The simplicity of the method assists in highlighting the etiology of the ADE by identifying structurally similar drugs. A set of drugs with strong evidence from both AERS and molecular fingerprint-based modeling is constructed for further analysis. Conclusion The results demonstrate that the proposed methodology could be used as a pharmacovigilance decision support tool to facilitate ADE detection.
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