UMMS Affiliation

Department of Population and Quantitative Health Sciences

Publication Date

2021-01-25

Document Type

Conference Proceeding

Disciplines

Artificial Intelligence and Robotics | Chemicals and Drugs | Health Information Technology | Health Services Research | Patient Safety

Abstract

A reliable and searchable knowledge database of adverse drug reactions (ADRs) is highly important and valuable for improving patient safety at the point of care. In this paper, we proposed a neural multi-task learning system, NeuroADR, to extract ADRs as well as relevant modifiers from free-text drug labels. Specifically, the NeuroADR system exploited a hierarchical multi-task learning (HMTL) framework to perform named entity recognition (NER) and relation extraction (RE) jointly, where interactions among the learned deep encoder representations from different subtasks are explored. Different from the conventional HMTL approach, NeuroADR adopted a novel task decomposition strategy to generate auxiliary subtasks for more inter-task interactions and integrated a new label encoding schema for better handling discontinuous entities. Experimental results demonstrate the effectiveness of the proposed system.

Keywords

adverse drug reactions, patient safety, neural multi-task learning system, drug labels

Rights and Permissions

Copyright © 2020 AMIA. This is an Open Access article: verbatim copying and redistribution of this article are permitted in all media for any purpose.

Source

Liu F, Zheng X, Yu H, Tjia J. Neural Multi-Task Learning for Adverse Drug Reaction Extraction. AMIA Annu Symp Proc. 2021 Jan 25;2020:756-762. PMID: 33936450; PMCID: PMC8075418.

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Journal/Book/Conference Title

AMIA ... Annual Symposium proceedings. AMIA Symposium

PubMed ID

33936450

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