Clinical Validation of Explainable Deep Learning Model for Predicting the Mortality of In-Hospital Cardiac Arrest Using Diagnosis Codes of Electronic Health Records.

Journal: Reviews in cardiovascular medicine
Published Date: Sep 21, 2023

Abstract

BACKGROUND: Using deep learning for disease outcome prediction is an approach that has made large advances in recent years. Notwithstanding its excellent performance, clinicians are also interested in learning how input affects prediction. Clinical validation of explainable deep learning models is also as yet unexplored. This study aims to evaluate the performance of Deep SHapley Additive exPlanations (D-SHAP) model in accurately identifying the diagnosis code associated with the highest mortality risk.

Authors

  • Chien-Yu Chi
    Department of Emergency Medicine, National Taiwan University Hospital Yunlin Branch, 640 Yunlin, Taiwan.
  • Hadi Moghadas-Dastjerdi
    Knowtions Research Inc., Toronto, Ontario M5J 2S1, Canada.
  • Adrian Winkler
    Knowtions Research Inc., Toronto, Ontario M5J 2S1, Canada.
  • Shuang Ao
    Knowtions Research Inc., Toronto, Ontario M5J 2S1, Canada.
  • Yen-Pin Chen
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Liang-Wei Wang
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Pei-I Su
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Wei-Shu Lin
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Min-Shan Tsai
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.
  • Chien-Hua Huang
    Department of Emergency Medicine, National Taiwan University, 100 Taipei, Taiwan.

Keywords

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