UMass Chan Affiliations
Department of Population and Quantitative Health SciencesDocument Type
Conference PaperPublication Date
2021-01-25Keywords
adverse drug reactionspatient safety
neural multi-task learning system
drug labels
Artificial Intelligence and Robotics
Chemicals and Drugs
Health Information Technology
Health Services Research
Patient Safety
Metadata
Show full item recordAbstract
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.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.
.