Prediction of mortality events of patients with acute heart failure in intensive care unit based on deep neural network.

Journal: Computer methods and programs in biomedicine
PMID: 39236563

Abstract

BACKGROUND: Acute heart failure (AHF) in the intensive care unit (ICU) is characterized by its criticality, rapid progression, complex and changeable condition, and its pathophysiological process involves the interaction of multiple organs and systems. This makes it difficult to predict in-hospital mortality events comprehensively and accurately. Traditional analysis methods based on statistics and machine learning suffer from insufficient model performance, poor accuracy caused by prior dependence, and difficulty in adequately considering the complex relationships between multiple risk factors. Therefore, the application of deep neural network (DNN) techniques to the specific scenario, predicting mortality events of patients with AHF under intensive care, has become a research frontier.

Authors

  • Jicheng Huang
    School of Life Sciences, Central South University, Changsha, China.
  • Yufeng Cai
    School of Life Sciences, Central South University, Changsha, China.
  • Xusheng Wu
    The First Clinical Medical College of Gansu University of Chinese Medicine, Gansu Provincial Hospital, Lanzhou, China.
  • Xin Huang
    Department of ophthalmology, Jiangxi Provincial People's Hospital, The First Affiliated Hospital of Nanchang Medical College, Nanchang, China.
  • Jianwei Liu
  • Dehua Hu
    Institute of Information Security and Big Data, Central South University, Changsha 410083, Hunan, China.

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