Interpretable Machine Learning Approach for Predicting 30-Day Mortality of Critical Ill Patients with Pulmonary Embolism and Heart Failure: A Retrospective Study.

Journal: Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/Hemostasis
PMID: 39633282

Abstract

BACKGROUND: Pulmonary embolism (PE) patients combined with heart failure (HF) have been reported to have a high short-term mortality. However, few studies have developed predictive tools of 30-day mortality for these patients in intensive care unit (ICU). This study aimed to construct and validate a machine learning (ML) model to predict 30-day mortality for PE patients combined with HF in ICU.

Authors

  • Jing Liu
    Department of Ophthalmology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China.
  • Ruobei Li
    Department of Cardiovascular Medicine, Hebei General Hospital, Shijiazhuang, Hebei, China.
  • Tiezhu Yao
    Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Provence, People's Republic of China.
  • Guang Liu
    Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education, Beijing Advanced Innovation Centre for Biomedical Engineering, School of Engineering Medicine, School of Biological Science and Medical Engineering, Beihang University, No.37 Xueyuan Road, Haidian District, Beijing, 100083, People's Republic of China.
  • Ling Guo
    Department of Nephrology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Ji'nan, China. Electronic address: gulixiji@sdu.edu.cn.
  • Jing He
    School of Management, Guilin University of Aerospace Technology, Guilin, China.
  • Zhengkun Guan
    Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Provence, People's Republic of China.
  • Shaoyan Du
    Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Provence, People's Republic of China.
  • Jingtao Ma
    Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Provence, People's Republic of China.
  • Zhenli Li
    Department of Cardiology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei Provence, People's Republic of China.

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