A new risk assessment model of venous thromboembolism by considering fuzzy population.

Journal: BMC medical informatics and decision making
PMID: 39736732

Abstract

BACKGROUND: Inpatients with high risk of venous thromboembolism (VTE) usually face serious threats to their health and economic conditions. Many studies using machine learning (ML) models to predict VTE risk overlook the impact of class-imbalance problem due to the low incidence rate of VTE, resulting in inferior and unstable model performance, which hinders their ability to replace the Padua model, a widely used linear weighted model in clinic. Our study aims to develop a new VTE risk assessment model suitable for Chinese medical inpatients.

Authors

  • Xin Wang
    Key Laboratory of Bio-based Material Science & Technology (Northeast Forestry University), Ministry of Education, Harbin 150040, China.
  • Yu-Qing Yang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, Beijing, China.
  • Xin-Yu Hong
    Department of Respiration, Peking Union Medical College Hospital, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Si-Hua Liu
    Department of Respiration, Peking Union Medical College Hospital, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China.
  • Jian-Chu Li
    Department of Ultrasound, State Key Laboratory of Complex Severe and Rare Diseases, Peking Union Medical College Hospital, Chinese Academy of Medical Science and Peking Union Medical College, No. 1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. jianchu.li@163.com.
  • Ting Chen
    CAS Key Laboratory of Tropical Marine Bio-resources and Ecology (LMB), Guangdong Provincial Key Laboratory of Applied Marine Biology (LAMB), South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou 510301, China. chan1010@scsio.ac.cn.
  • Ju-Hong Shi
    Department of Respiration, Peking Union Medical College Hospital, No.1, Shuaifuyuan, Dongcheng District, Beijing, 100730, China. shijh@pumch.cn.

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