Extracting Adverse Drug Events from Clinical Notes

Abstract

Adverse drug events (ADEs) are unexpected incidents caused by the administration of a drug or medication. To identify and extract these events, we require information about not just the drug itself but attributes describing the drug (e.g., strength, dosage), the reason why the drug was initially prescribed, and any adverse reaction to the drug. This paper explores the relationship between a drug and its associated attributes using relation extraction techniques. We explore three approaches: a rule-based approach, a deep learning-based approach, and a contextualized language model-based approach. We evaluate our system on the n2c2-2018 ADE extraction dataset. Our experimental results demonstrate that the contextualized language model-based approach outperformed other models overall and obtain the state-of-the-art performance in ADE extraction with a Precision of 0.93, Recall of 0.96, and an F 1 score of 0.94; however, for certain relation types, the rule-based approach obtained a higher Precision and Recall than either learning approach. This talk won Third place in the Best student paper award competition. link: https://www.amia.org/summit2021/award-winners

Samantha (Darshini) Mahendran

Graduate Research Assistant

My research interests include distributed robotics, mobile computing and programmable matter.