Predicting a failure of postoperative thromboprophylaxis in non-small cell lung cancer: A stacking machine learning approach.

Journal: PloS one
PMID: 40168285

Abstract

BACKGROUND: Non-small-cell lung cancer (NSCLC) and its surgery significantly increase the venous thromboembolism (VTE) risk. This study explored the VTE risk factors and established a machine-learning model to predict a failure of postoperative thromboprophylaxis.

Authors

  • Ligang Hao
    Department of Thoracic Surgery, Xing Tai People's Hospital, Xing Tai, He Bei, China.
  • Junjie Zhang
    Department of Immunology, School of Basic Medical Sciences, Anhui Medical University, Hefei, 230032, PR China.
  • Yonghui Di
    Department of Thoracic Surgery, Xingtai People's Hospital, Xingtai, Hebei, China.
  • Zheng Qi
    Department of Clinical Lab, Xingtai People's Hospital, Xingtai, Hebei, China.
  • Peng Zhang
    Key Laboratory of Macromolecular Science of Shaanxi Province, School of Chemistry & Chemical Engineering, Shaanxi Normal University, Xi'an, Shaanxi 710062, China.

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