Normalizing Adverse Events using Recurrent Neural Networks with Attention

Abstract

Adverse events (AEs) are undesirable outcomes of medication administration and cause many hospitalizations as well as even deaths per year. Information about AEs can enable their prevention. Natural language processing (NLP) techniques can identify AEs from narratives and match them to a structured terminology. We propose a novel neural network for AE normalization utilizing bidirectional long short-term memory (biLSTM) with attention mechanism that generalizes to diverse datasets. We train this network to first learn a framework for general AE normalization and then to learn the specifics of the task on individual corpora. Our results on the datasets from the Text Analysis Conference (TAC) 2017-ADR track, FDA adverse drug event evaluation shared task, and the Social Media Mining for Health Applications Workshop & Shared Task 2019 show that our approach outperforms widely used rule-based normalizers on a diverse set of narratives. Additionally, it outperforms the best normalization system by 4.86 in macro-averaged F1-score in the TAC 2017-ADR track.

[Figure 1]

AE description - MedDRA PT collection from UMLS

[Figure 2]

Overview of the structure of our normalizer