Harness machine learning for multiple prognoses prediction in sepsis patients: evidence from the MIMIC-IV database.

Journal: BMC medical informatics and decision making
PMID: 40165185

Abstract

BACKGROUND: Sepsis, a severe systemic response to infection, frequently results in adverse outcomes, underscoring the urgency for prompt and accurate prognostic tools. Machine learning methods such as logistic regression, random forests, and CatBoost, have shown potential in early sepsis prediction. The study aimed to create and verify a machine learning model capable of early prognostic identification of patients with sepsis in intensive care units (ICUs).

Authors

  • Su-Zhen Zhang
    Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
  • Hai-Yi Ding
    Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
  • Yi-Ming Shen
    Department of Emergency Center, Second Affiliated Hospital of Nantong University, No. 6 North, Child Lane Road, Nantong, China.
  • Bing Shao
    Affiliated Hospital of Nanjing University of Chinese Medicine, Nanjing, Jiangsu Province, China.
  • Yuan-Yuan Gu
    Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Han Zhong Road, Nanjing, Jiangsu Province, China.
  • Qiu-Hua Chen
    Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Han Zhong Road, Nanjing, Jiangsu Province, China.
  • Hai-Dong Zhang
    Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Han Zhong Road, Nanjing, Jiangsu Province, China.
  • Ying-Hao Pei
    Department of Intensive Care Unit, Jiangsu Province Hospital of Chinese Medicine, Affiliated Hospital of Nanjing University of Chinese Medicine, 155 Han Zhong Road, Nanjing, Jiangsu Province, China. piaopiao5556@njucm.edu.cn.
  • Hua Jiang
    Institute for Emergency and Disaster Medicine, Sichuan Academy of Medical Sciences, Sichuan Provincial People's Hospital, School of Medicine, University of Electronic Science and Technology of China, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China; Sino-Finnish Medical AI Research Center, No. 32, Yi Huan Lu Xi Er Duan, Chengdu, Sichuan Province, China. Electronic address: hua.jiang@traumabank.org.

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