Objective:  Our goal is to develop a natural language processing (NLP) algorithm to detect adverse drug events from hospital discharge summaries. Method: An NLP algorithm was developed using customised dictionaries of drugs, adverse event (AE) terms, and rules based on trigger phrases, negations, fuzzy logic and word distances. Results: A customised annotation tool facilitated expert review of discharge summaries from a tertiary hospital in Singapore in 2011.  A total of 33 trial sets with 50 to 100 records per set were evaluated (1,620 discharge summaries).  Pharmacovigilance experts reviewed the NLP results using a customised entity recognition, relationship detection and annotation tool named REAP (REadpeer for Active Pharmacovigilance).  After every 6 trial sets, drug and AE dictionaries were updated, and rules were modified to improve the performance of REAP.  Excellent performance was achieved for drug and AE entity recognition with over 92% precision and recall.  On the final 6 sets of discharge summaries (600 records), REAP achieved 75% precision and 59% recall for identification of valid drug-AE relations.  Conclusions: Adverse drug reactions are a significant contributor to health care costs and utilization. REAP is not restricted to particular drugs, drug classes or specific medical specialties, which is an important attribute for a national regulatory authority to carry out comprehensive safety monitoring of drug products. Drug and AE dictionaries may be updated periodically to ensure that the tool remains relevant for performing surveillance activities. The development of REAP, and the ease of reviewing and correcting the results of the algorithm as part of an iterative machine learning process, is an important step towards use of hospital discharge summaries for an active pharmacovigilance programme.

Text Mining Discharge Summaries of Hospital Electronic Medical Records to Detect Adverse Drug Reactions

DIA Europe 2019

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