Multi-horizon event detection for in-hospital clinical deterioration using dual-channel graph attention network.

Journal: International journal of medical informatics
PMID: 39657403

Abstract

OBJECTIVE: In hospitals globally, the occurrence of clinical deterioration within the hospital setting poses a significant healthcare burden. Rapid clinical intervention becomes a crucial task in such cases. In this research, we propose an end-to-end deep learning architecture that interpolates high-dimensional sequential data for the early detection of clinical deterioration events.

Authors

  • Thanh-Cong Do
    The Department of Artificial Intelligence Convergence, Chonnam National University, Gwangju, South Korea.
  • Hyung-Jeong Yang
    Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500-757, Korea.
  • Soo-Hyung Kim
    Department of Artificial Intelligence Convergence, Chonnam National University, 77 Yongbong-ro, Gwangju 500-757, Korea.
  • Bo-Gun Kho
    The Department of Internal Medicine, Chonnam National University Hospital, Gwangju, South Korea.
  • Jin-Kyung Park
    The College of Nursing, Chonnam National University Hospital, Gwangju, South Korea.

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