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    Neural Multi-Task Learning for Adverse Drug Reaction Extraction

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    Authors
    Liu, Feifan
    Zheng, Xiaoyu
    Yu, Hong
    Tjia, Jennifer
    UMass Chan Affiliations
    Department of Population and Quantitative Health Sciences
    Document Type
    Conference Paper
    Publication Date
    2021-01-25
    Keywords
    adverse drug reactions
    patient safety
    neural multi-task learning system
    drug labels
    Artificial Intelligence and Robotics
    Chemicals and Drugs
    Health Information Technology
    Health Services Research
    Patient Safety
    
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    Link to Full Text
    https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8075418/
    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.
    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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    Permanent Link to this Item
    http://hdl.handle.net/20.500.14038/46934
    PubMed ID
    33936450
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    Link to Article in PubMed

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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.
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