Predicting the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Journal: European journal of medical research
PMID: 39710777

Abstract

BACKGROUND: This study aimed to develop predictive models with robust generalization capabilities for assessing the risk of pulmonary embolism in patients with tuberculosis using machine learning algorithms.

Authors

  • Haobo Kong
    Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China.
  • Yong Li
    Department of Surgical Sciences, Western Michigan University Homer Stryker M.D. School of Medicine, Kalamazoo, MI, United States.
  • Ya Shen
    Division of Endodontics, Department of Oral Biological and Medical Sciences, Faculty of Dentistry, University of British Columbia, Vancouver, BC, Canada.
  • Jingjing Pan
    Department of Respiratory Intensive Care Unit, Anhui Medical University Clinical College of Chest & Anhui Chest Hospital, Hefei, 230022, China.
  • Min Liang
    Department of Respiratory and Critical Care Medicine, Maoming People's Hospital, Maoming, China.
  • Zhi Geng
    Department of Neurology, The First Affiliated Hospital of Anhui Medical University, Hefei, China. gengzhi2017@163.com.
  • Yanbei Zhang
    Department of Geriatric Respiratory and Critical Care, Anhui Geriatric Institute, The First Affiliated Hospital of Anhui Medical University, Hefei, 230022, Anhui, China. zhangyanbei1963@126.com.

Keywords

External Resources

Popular Topics
Recent Journals