Machine Learning-Based Mortality Risk Prediction Model in Patients with Sepsis.

Journal: Journal of inflammation research
Published Date: May 19, 2025

Abstract

OBJECTIVE: The aim of our study was to establish and validate a machine learning-based predictive model for mortality risk in elderly patients with sepsis. By integrating traditional biomarkers, novel biomarkers, clinical data, and established scoring systems, the model seeks to enhance predictive accuracy and thereby improve clinical outcomes in high-risk patient population.

Authors

  • Ye Zhang
    Department of Radiation Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China.
  • Chen Li
    School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, Shaanxi, China.
  • Yilin Ji
    Shandong University of Traditional Chinese Medicine College of Optometry and Ophthalmology, Jinan, Shandong Province, 250355, People's Republic of China.
  • Bing Wei
  • Shubin Guo
    Emergency Medicine Clinical Research Center, Beijing Key Laboratory of Cardiopulmonary Cerebral Resuscitation, Beijing Chaoyang Hospital, Affiliated to Capital Medical University, Beijing, 100020, China. shubin007@yeah.net.
  • Xue Mei
    Center for Hypertension and Precision Medicine, Department of Physiology and Pharmacology, University of Toledo College of Medicine and Life Sciences, Toledo, OH, USA.
  • Junyu Wang
    Department of Biomedical Engineering, University of Virginia, Charlottesville, Virginia, USA.

Keywords

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