UMMS Affiliation

Department of Population and Quantitative Health Sciences

Publication Date


Document Type

Conference Proceeding


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


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.


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

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


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.


Journal/Book/Conference Title

AMIA ... Annual Symposium proceedings. AMIA Symposium

PubMed ID


Related Resources

Link to Article in PubMed