Advancing shock prediction: leveraging prior knowledge and self-controlled data for enhanced model accuracy and generalizability.

Journal: BMC medical informatics and decision making
Published Date: Jul 14, 2025

Abstract

OBJECTIVES: Timely intervention in shock is vital, as delays over one hour greatly increase mortality. This study aims to develop an enhanced machine learning model that improves predictive performance by utilizing self-controlled data and applying feature engineering informed by medical knowledge to physiological waveforms, enabling the prediction of shock one hour in advance without relying on blood tests.

Authors

  • Cheng-Yu Tsai
    Graduate Institute of Clinical Medicine, College of Medicine, Kaohsiung Medical University, Kaohsiung, Taiwan.
  • Xiu-Rong Huang
    Advantech Co., Ltd., Taipei, Taiwan.
  • Po-Tsun Kuo
    Advantech Co., Ltd., Taipei, Taiwan.
  • Tzu-Tao Chen
    Division of Pulmonary Medicine, Department of Internal Medicine, Shuang Ho Hospital, New Taipei City, Taiwan.
  • Yun-Kai Yeh
    Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, 235041, Taiwan.
  • Kuan-Yuan Chen
    Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.
  • Arnab Majumdar
    Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK.
  • Chien-Hua Tseng
    Division of Pulmonary Medicine, Department of Internal Medicine, Taipei Medical University-Shuang Ho Hospital, New Taipei City, Taiwan.

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