Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning.

Journal: BMC medical informatics and decision making
Published Date: Jul 1, 2025

Abstract

BACKGROUND: Acute graft-versus-host disease (aGVHD) is a major post-transplantation complication and one of the most significant causes of non-relapse-related death. However, the massive and complex clinical data make aGVHD difficult to predict. Machine learning (ML), a branch of artificial intelligence, has since been introduced in medicine due to its ability to process complex, high-dimensional variables quickly and capture nonlinear relationships. However, the effects of immunosuppressants exposure was not considered in previous ML models. Thus, the purpose of this study was to develop and optimize models by Cox regression and machine learning algorithms to predict the risk of aGVHD in which cyclosporin A exposure and common clinical factors were included as variables.

Authors

  • Lin Song
    Department of Head and Neck Surgery, Renji Hospital, School of Medicine, Shanghai Jiaotong University, 160 Pujian Road, Pudong District, Shanghai, 200127, China.
  • Xingwei Wu
    School of Medicine, University of Electronic Science and Technology of China, Chengdu, China. wuxw1998@126.com.
  • Mengjia Xu
    Key Laboratory of Medical Image Computing of Ministry of Education, Northeastern University, Shenyang, China.
  • Ling Xue
  • Xun Yu
    Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100097, China.
  • Zongqi Cheng
    Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China.
  • Chenrong Huang
    Department of Pharmacy, The First Affiliated Hospital of Soochow University, Suzhou, 215006, China. huangchenrong@suda.edu.cn.
  • Liyan Miao
    Department of Pharmacy, First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China.

Keywords

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