research-article

Exploring Disorder-Aware Attention for Clinical Event Extraction

Authors:

Pralay Ramteke,

Pushpak BhattacharyyaAuthors Info & Claims

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Volume 16, Issue 1s

Article No.: 31, Pages 1 - 21

https://doi.org/10.1145/3372328

Published: 17 April 2020 Publication History

Abstract

Event extraction is one of the crucial tasks in biomedical text mining that aims to extract specific information concerning incidents embedded in the texts. In this article, we propose a deep learning framework that aims to identify the attributes (severity, course, temporal expression, and document creation time) associated with the medical concepts extracted from electronic medical records. The bi-directional long short-term memory network assisted by the attention mechanism is utilized to uncover the important aspects of the patient’s medical conditions. The attention mechanism specific to the medical disorder mention can focus on various parts of the sentence when different disorders are considered as input. The proposed methodology is evaluated on benchmark ShARe/CLEF eHealth Evaluation Lab 2014 shared task 2 datasets. In addition to the CLEF dataset, we also used the social media text, especially the medical blog posts. Experimental results of the proposed approach illustrate that our proposed approach achieves significant performance improvements over the state-of-the-art techniques and the highly competitive deep learning--based baseline methods.

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Cited By

Zhang BSun XLi XLiu DWang SJia J(2022)Construction and Application of Event Logic Graph: A SurveyDatabase Systems for Advanced Applications. DASFAA 2022 International Workshops10.1007/978-3-031-11217-1_12(160-174)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-11217-1_12
Zhai PWang CFang Y(2021)LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event ExtractionApplied Sciences10.3390/app1116723711:16(7237)Online publication date: 5-Aug-2021
https://doi.org/10.3390/app11167237
Yadav SLokala UDaniulaityte RThirunarayan KLamy FSheth A(2021)“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware attention framework for relationship extractionPLOS ONE10.1371/journal.pone.024829916:3(e0248299)Online publication date: 25-Mar-2021
https://doi.org/10.1371/journal.pone.0248299
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Index Terms

Exploring Disorder-Aware Attention for Clinical Event Extraction
1. Computer systems organization
  1. Dependable and fault-tolerant systems and networks
    1. Redundancy
  2. Embedded and cyber-physical systems
    1. Embedded systems
    2. Robotics
2. Networks
  1. Network properties
    1. Network reliability

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Published In

cover image ACM Transactions on Multimedia Computing, Communications, and Applications

ACM Transactions on Multimedia Computing, Communications, and Applications Volume 16, Issue 1s

Special Issue on Multimodal Machine Learning for Human Behavior Analysis and Special Issue on Computational Intelligence for Biomedical Data and Imaging

January 2020

376 pages

ISSN:1551-6857

EISSN:1551-6865

DOI:10.1145/3388236

Editor:
Alberto Del Bimbo
University of Firenze, Italy

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Copyright © 2020 ACM.

© 2020 Association for Computing Machinery. ACM acknowledges that this contribution was authored or co-authored by an employee, contractor or affiliate of a national government. As such, the Government retains a nonexclusive, royalty-free right to publish or reproduce this article, or to allow others to do so, for Government purposes only.

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Association for Computing Machinery

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Publication History

Published: 17 April 2020

Accepted: 01 November 2019

Revised: 01 November 2019

Received: 01 May 2019

Published in TOMM Volume 16, Issue 1s

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Cited By

Zhang BSun XLi XLiu DWang SJia J(2022)Construction and Application of Event Logic Graph: A SurveyDatabase Systems for Advanced Applications. DASFAA 2022 International Workshops10.1007/978-3-031-11217-1_12(160-174)Online publication date: 11-Apr-2022
https://dl.acm.org/doi/10.1007/978-3-031-11217-1_12
Zhai PWang CFang Y(2021)LSLSD: Fusion Long Short-Level Semantic Dependency of Chinese EMRs for Event ExtractionApplied Sciences10.3390/app1116723711:16(7237)Online publication date: 5-Aug-2021
https://doi.org/10.3390/app11167237
Yadav SLokala UDaniulaityte RThirunarayan KLamy FSheth A(2021)“When they say weed causes depression, but it’s your fav antidepressant”: Knowledge-aware attention framework for relationship extractionPLOS ONE10.1371/journal.pone.024829916:3(e0248299)Online publication date: 25-Mar-2021
https://doi.org/10.1371/journal.pone.0248299
Li XWen QLin HJiao ZZhang J(2021)Overview of CCKS 2020 Task 3: Named Entity Recognition and Event Extraction in Chinese Electronic Medical RecordsData Intelligence10.1162/dint_a_000933:3(376-388)Online publication date: 8-Sep-2021
https://doi.org/10.1162/dint_a_00093
Gupta DSuman SEkbal A(2021)Hierarchical deep multi-modal network for medical visual question answeringExpert Systems with Applications10.1016/j.eswa.2020.113993164(113993)Online publication date: Feb-2021
https://doi.org/10.1016/j.eswa.2020.113993
de Oliveira Jda Costa CAntunes R(2021)Data structuring of electronic health records: a systematic reviewHealth and Technology10.1007/s12553-021-00607-wOnline publication date: 29-Oct-2021
https://doi.org/10.1007/s12553-021-00607-w

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