A deep learning approach for acute liver failure prediction with combined fully connected and convolutional neural networks.

Journal: Technology and health care : official journal of the European Society for Engineering and Medicine
Published Date: Jan 1, 2024

Abstract

BACKGROUND: Acute Liver Failure (ALF) is a critical medical condition with rapid development, often caused by viral infections, hepatotoxic drug abuse, or other severe liver diseases. Timely and accurate prediction of ALF occurrence is clinically crucial. However, predicting ALF poses challenges due to the diverse physiological differences among patients and the dynamic nature of the disease.

Authors

  • Hefu Xie
    Xiamen University School of Information Science and Technology, Xiamen University Xiang'an Campus, Xiamen, Fujian, China.
  • Bingbing Wang
    Department of Thoracic Surgery, The First Hospital Affiliated of Xiamen University, School of Medicine, Xiamen University, Xiamen, Fujian, China.
  • Yuanzhen Hong
    Hepatology Department's Three Wards, Xiamen Hospital, Beijing University of Chinese Medicine, Xiamen, Fujian, China.

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