Machine learning-based model for predicting all-cause mortality in severe pneumonia.

Journal: BMJ open respiratory research
PMID: 40122535

Abstract

BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians in classification and management decisions. The goal of this study was to analyse the clinical characteristics of severe pneumonia and develop a machine learning-based mortality-prediction model for patients with severe pneumonia.

Authors

  • Weichao Zhao
    Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China.
  • Xuyan Li
    Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China.
  • Lianjun Gao
    Beijing Boai hospital, Department of Respiratory and Critical Care Medicine, Beijing, China.
  • Zhuang Ai
    Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China.
  • Yaping Lu
    Sinopharm Genomics Technology Co Ltd, Changzhou, Jiangsu, China.
  • Jiachen Li
    Department of Clinical Epidemiology, Capital Medical University, Beijing, China.
  • Dong Wang
    Department of Neurosurgery, Tianjin Medical University General Hospital, Tianjin, China.
  • Xinlou Li
    Department of Medical Research, the Ninth Medical Center, Chinese PLA General Hospital, Beijing, China.
  • Nan Song
    Department of Thoracic Surgery, Shanghai Pulmonary Hospital, Tongji University, Shanghai 200433, China.
  • Xuan Huang
    Department of Gastroenterology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, China.
  • Zhao-Hui Tong
    Department of Respiratory and Critical Care Medicine, Capital Medical University, Beijing, China tongzhaohuicy@sina.com huangxuan03@163.com.

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