Machine learning-based derivation and validation of three immune phenotypes for risk stratification and prognosis in community-acquired pneumonia: a retrospective cohort study.

Journal: Frontiers in immunology
PMID: 39114653

Abstract

BACKGROUND: The clinical presentation of Community-acquired pneumonia (CAP) in hospitalized patients exhibits heterogeneity. Inflammation and immune responses play significant roles in CAP development. However, research on immunophenotypes in CAP patients is limited, with few machine learning (ML) models analyzing immune indicators.

Authors

  • Qiangqiang Qin
    Department of Respiratory Medicine, Xinhua Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China.
  • Haiyang Yu
    State Key Laboratory of Component-based Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, China.
  • Jie Zhao
    Department of Liver & Gallbladder Surgery, The First People's Hospital, Shangqiu, Henan, China.
  • Xue Xu
    Key Laboratory of Chinese Internal Medicine of Ministry of Education and Beijing, Dongzhimen Hospital, Beijing University of Chinese Medicine, Beijing, 100700, China; Marcus Institute for Aging Research, Hebrew SeniorLife and Harvard Medical School, Boston, MA, 02131, USA.
  • Qingxuan Li
    State Key Laboratory of ASIC and System, School of Microelectronics, Fudan University, Shanghai 200433, P.R. China.
  • Wen Gu
    Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China.
  • Xuejun Guo
    Department of Respiratory Medicine, Xinhua Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200092, China. guoxuejun@xinhuamed.com.cn.

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