A pre-trained language model for emergency department intervention prediction using routine physiological data and clinical narratives.

Journal: International journal of medical informatics
Published Date:

Abstract

INTRODUCTION: The urgency and complexity of emergency room (ER) settings require precise and swift decision-making processes for patient care. Ensuring the timely execution of critical examinations and interventions is vital for reducing diagnostic errors, but the literature highlights a need for innovative approaches to optimize diagnostic accuracy and patient outcomes. In response, our study endeavors to create predictive models for timely examinations and interventions by leveraging the patient's symptoms and vital signs recorded during triage, and in so doing, augment traditional diagnostic methodologies.

Authors

  • Ting-Yun Huang
    Shuang-Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
  • Chee-Fah Chong
    Emergency Department, Shin-Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan. Electronic address: m002202@ms.skh.org.tw.
  • Heng-Yu Lin
    Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan. Electronic address: m946110012@tmu.edu.tw.
  • Tzu-Ying Chen
    Graduate Institute of Data Science, Taipei Medical University, Taipei City, Taiwan.
  • Yung-Chun Chang
    Graduate Institute of Data Science, College of Management, Taipei Medical University, Taipei, Taiwan.
  • Ming-Chin Lin
    Graduate Institute of Biomedical Informatics, College of Medicine Science and Technology, Taipei Medical University, Taipei, Taiwan; Division of Neurosurgery, Department of Surgery, Shuang-Ho Hospital, Taipei, Taiwan; International Center for Health Information Technology (ICHIT), Taipei Medical University, Taipei, Taiwan.