research-article

Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke

Authors:

Forhan Bin Emdad,

Zhe HeAuthors Info & Claims

BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

Article No.: 26, Pages 1 - 6

https://doi.org/10.1145/3584371.3613002

Published: 04 October 2023 Publication History

Abstract

Stroke is a significant cause of mortality and morbidity, necessitating early predictive strategies to minimize risks. Traditional methods for evaluating patients, such as Acute Physiology and Chronic Health Evaluation (APACHE II, IV) and Simplified Acute Physiology Score III (SAPS III), have limited accuracy and interpretability. This paper proposes a novel approach: an interpretable, attention-based transformer model for early stroke mortality prediction. This model seeks to address the limitations of previous predictive models, providing both interpretability (providing clear, understandable explanations of the model) and fidelity (giving a truthful explanation of the model's dynamics from input to output). Furthermore, the study explores and compares fidelity and interpretability scores using Shapley values and attention-based scores to improve model explainability. The research objectives include designing an interpretable attention-based transformer model, evaluating its performance compared to existing models, and providing feature importance derived from the model.

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Cited By

Fantozzi PNaldi M(2024)The Explainability of Transformers: Current Status and DirectionsComputers10.3390/computers1304009213:4(92)Online publication date: 4-Apr-2024
https://doi.org/10.3390/computers13040092
Ding JThao PPeng WWang JChug CHsieh MTseng YChen LLuo DWu CWang CHsu CChen YChen PLiu FHung F(2024)Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health recordsScientific Reports10.1038/s41598-024-71020-214:1Online publication date: 6-Sep-2024
https://doi.org/10.1038/s41598-024-71020-2
Rahman MEmdad FIslam CHe Z(2024)Uncovering the Interplay of Demographics and Healthcare Provider Availability on CMS HCC Risk Scores for Disabled BeneficiariesAdvances in Digital Health and Medical Bioengineering10.1007/978-3-031-62520-6_66(593-600)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-62520-6_66

Index Terms

Can Attention Be Used to Explain EHR-Based Mortality Prediction Tasks: A Case Study on Hemorrhagic Stroke
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks

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cover image ACM Conferences

BCB '23: Proceedings of the 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

September 2023

626 pages

ISBN:9798400701269

DOI:10.1145/3584371

General Chairs:
May D. Wang
SGeorgia Institute of Technology
,
Byung-Jun Yoon

Copyright © 2023 ACM.

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Published: 04 October 2023

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National Center for Advancing Translational Sciences
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BCB '23: 14th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics

September 3 - 6, 2023

TX, Houston, USA

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Cited By

Fantozzi PNaldi M(2024)The Explainability of Transformers: Current Status and DirectionsComputers10.3390/computers1304009213:4(92)Online publication date: 4-Apr-2024
https://doi.org/10.3390/computers13040092
Ding JThao PPeng WWang JChug CHsieh MTseng YChen LLuo DWu CWang CHsu CChen YChen PLiu FHung F(2024)Large language multimodal models for new-onset type 2 diabetes prediction using five-year cohort electronic health recordsScientific Reports10.1038/s41598-024-71020-214:1Online publication date: 6-Sep-2024
https://doi.org/10.1038/s41598-024-71020-2
Rahman MEmdad FIslam CHe Z(2024)Uncovering the Interplay of Demographics and Healthcare Provider Availability on CMS HCC Risk Scores for Disabled BeneficiariesAdvances in Digital Health and Medical Bioengineering10.1007/978-3-031-62520-6_66(593-600)Online publication date: 31-Aug-2024
https://doi.org/10.1007/978-3-031-62520-6_66

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