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Temporal Relation Extraction in Clinical Texts: A Systematic Review

Authors:

Yohan Bonescki Gumiel,

Lucas Emanuel Silva e Oliveira,

Vincent Claveau,

Natalia Grabar,

Emerson Cabrera Paraiso,

Deborah Ribeiro CarvalhoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 54, Issue 7

Article No.: 144, Pages 1 - 36

https://doi.org/10.1145/3462475

Published: 17 September 2021 Publication History

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Abstract

Unstructured data in electronic health records, represented by clinical texts, are a vast source of healthcare information because they describe a patient's journey, including clinical findings, procedures, and information about the continuity of care. The publication of several studies on temporal relation extraction from clinical texts during the last decade and the realization of multiple shared tasks highlight the importance of this research theme. Therefore, we propose a review of temporal relation extraction in clinical texts. We analyzed 105 articles and verified that relations between events and document creation time, a coarse temporality type, were addressed with traditional machine learning–based models with few recent initiatives to push the state-of-the-art with deep learning–based models. For temporal relations between entities (event and temporal expressions) in the document, factors such as dataset imbalance because of candidate pair generation and task complexity directly affect the system's performance. The state-of-the-art resides on attention-based models, with contextualized word representations being fine-tuned for temporal relation extraction. However, further experiments and advances in the research topic are required until real-time clinical domain applications are released. Furthermore, most of the publications mainly reside on the same dataset, hindering the need for new annotation projects that provide datasets for different medical specialties, clinical text types, and even languages.

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Index Terms

Temporal Relation Extraction in Clinical Texts: A Systematic Review
1. Applied computing
  1. Life and medical sciences
    1. Health informatics
2. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
      1. Information extraction

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cover image ACM Computing Surveys

ACM Computing Surveys Volume 54, Issue 7

September 2022

778 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3476825

Editor:
Albert Zomaya
University of Sydney, Australia

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Published: 17 September 2021

Accepted: 01 April 2021

Revised: 01 March 2021

Received: 01 February 2019

Published in CSUR Volume 54, Issue 7

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https://dl.acm.org/doi/10.1145/3589335.3651256
Huang TSocrates VGilson ASafranek CChi LWang EPuglisi LBrandt CTaylor RWang K(2024)Identifying incarceration status in the electronic health record using large language models in emergency department settingsJournal of Clinical and Translational Science10.1017/cts.2024.4968:1Online publication date: 11-Mar-2024
https://doi.org/10.1017/cts.2024.496
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https://doi.org/10.1016/j.jbi.2024.104667
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