Construction and verification of a machine learning-based prediction model of deep vein thrombosis formation after spinal surgery.

Journal: International journal of medical informatics
PMID: 39260049

Abstract

BACKGROUND: Deep vein thromboembolism (DVT) is a common postoperative complication with high morbidity and mortality rates. However, the safety and effectiveness of using prophylactic anticoagulants for preventing DVT after spinal surgery remain controversial. Hence, it is crucial to predict whether DVT occurs in advance following spinal surgery. The present study aimed to establish a machine learning (ML)-based prediction model of DVT formation following spinal surgery.

Authors

  • Xingyan Wu
    Department of Anesthesiology, Second Affiliated Hospital of Zunyi Medical University, Guizhou Province, China. Electronic address: 1028658772@qq.com.
  • Zhao Wang
    Department of Urology, Xiangya Hospital, Central South University, Changsha, China.
  • Leilei Zheng
    Department of Orthodontics, Stomatological Hospital of Chongqing Medical University Chongqing Key Laboratory of Oral Disease and Biomedical Sciences Chongqing Municipal Key Laboratory of Oral Biomedical Engineering of Higher Education, Chongqing, China. Electronic address: zhengleileicqmu@hospital.cqmu.edu.cn.
  • Yihui Yang
    Department of Anesthesiology, Third Affiliated Hospital of Zunyi Medical University, Guizhou Province, China.
  • Wenyan Shi
    Department of Anesthesiology, Second Affiliated Hospital of Zunyi Medical University, Guizhou Province, China.
  • Jing Wang
    Endoscopy Center, Peking University Cancer Hospital and Institute, Beijing, China.
  • Dexing Liu
    School of Electronic and Computer Engineering, Peking University, Shenzhen 518055, China.
  • Yi Zhang
    Department of Thyroid Surgery, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, China.

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