| Summary: | Background: An extensive amount of data derived from medical case reports regarding potential adverse events is subjected to manual review. Devising efficient strategies for identification and information extraction concerning potential adverse events are needed to support timely monitoring of the reports and decision making. Methods: This paper aims at providing a machine learning (ML) and natural language processing (NLP) based solution for extracting suspect drugs and adverse events. The solution is based upon two approaches: Causal Sentence Classification classifies the relationship between drug and medical condition as causal or non-causal, and Suspect Drug Identification classifies each drug present in the report as a suspect drug or non-suspect drug. Results: Causal Sentence Classification yielded a precision of 0.85 and recall of 0.84 in establishing causality between drugs and medical conditions on the testing dataset consisting of 6252 records. After evaluation on a reliable testing dataset of 3522 records, the Suspect Drug Identification successfully identified suspect drugs with a precision of 0.72 and recall of 0.77. Conclusions: The developed solution relies on semantic and syntactic based features to capture the writing style of incoming reports, and showcases the potential of ML and NLP for Pharmacovigilance. Keywords: Pharmacovigilance, Text analytics, Natural language processing, Machine learning
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