Machine Learning for In-hospital Mortality Prediction in Critically Ill Patients With Acute Heart Failure: A Retrospective Analysis Based on the MIMIC-IV Database.

Journal: Journal of cardiothoracic and vascular anesthesia
PMID: 39779429

Abstract

BACKGROUND: The incidence, mortality, and readmission rates for acute heart failure (AHF) are high, and the in-hospital mortality for AHF patients in the intensive care unit (ICU) is higher. However, there is currently no method to accurately predict the mortality of AHF patients.

Authors

  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Yiwu Sun
    Department of Anesthesiology, Dazhou Central Hospital, Dazhou, Sichuan, China.
  • Jie Ren
    Digital Clinical Measures, Translational Medicine, Merck & Co., Inc., Rahway, NJ, United States.
  • Yifan Wu
    Department of Information Science and Technology, Northwest University, Xi'an, Shaanxi 710127, China.
  • Zhaoyi He
    College of Civil Traffic & Transportation, Chongqing Jiaotong University, Chongqing, 400074, China.

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