Pharmacovigilance Project

We have already demonstrated that we can accurately encode information in clinical reports using the NLP system MedLEE, and that we can accurately detect associations among clinical events using statistical methods that we developed. MedLEE will be used to map comprehensive clinical information in the EHR to codified data, and then statistical methods will be used to generate an extensive knowledge base of disease-symptom, disease-drug, drug-drug, and drug-symptom associations, which will be used to discover new ADEs. Additionally, we will also apply clinical knowledge and statistical methods to reduce confounding, which is a challenge when using the clinical record because there are many interdependencies in the data. We will also develop methods to map fine-grained concepts into higher level concepts, which could be important for optimizing the statistical methods. The performance of our discovery methods is evaluated by testing the methods using drugs currently in use with known ADEs, and also by using historical rollback. We are currently focusing on discovery of serious adverse events using inpatient records, and will then use the longitudinal outpatient record.

Funding

This project is supported by grants T15-LM007079, R01 LM010016, R01 LM010016-0S1, R01 LM010016-0S2, R01 LM008635, and R01 LM06910 from the National Library of Medicine.

Publications

• Wang X, Hripcsak G, Markatou M, Friedman C. Active Computerized Pharmacovigilance using Natural Language Processing, Statistics, and Electronic Health Records: a Feasibility Study. J Am Med Inform Assoc2009 March 4. PMCID: PMC2732239.
• Wang X, Hripcsak G, Friedman C. Characterizing environmental and phenotypic associations using information theory and electronic health records. BMC Bioinformatics 2009;10 Suppl 9; S13. PMCID:PMC2745684.
• R, Haerian K, Chase HS, Friedman C. Mining multi-item drug adverse effect associations in spontaneous reporting systems. BMC Bioinformatics (in press).
• Wang X, Chase HS, Li J, Hripcsak G, Friedman C. Integrating heterogeneous knowledge sources to acquire executable drug-related knowledge. Proc 2010 AMIA Annu Symp (in press).
• Harpaz R, Haerian K, Chase HS, Friedman C. Statistical mining of potential drug interaction adverse effects in FDA’s spontaneous reporting system. Proc 2010 AMIA Annu Symp (in press).
• Harpaz R, Haerian K, Chase HS, Friedman C. Mining electronic health records for adverse drug effects using regression based methods. Proc 1st ACM International Health Informatics Symposium (IHI 2010) (in press).