Opioid Nonadherence Risk Prediction of Patients with Cancer-Related Pain Based on Five Machine Learning Algorithms.

Journal: Pain research & management
Published Date: Jun 6, 2024

Abstract

OBJECTIVES: Opioid nonadherence represents a significant barrier to cancer pain treatment efficacy. However, there is currently no effective prediction method for opioid adherence in patients with cancer pain. We aimed to develop and validate a machine learning (ML) model and evaluate its feasibility to predict opioid nonadherence in patients with cancer pain.

Authors

  • Jinmei Liu
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology (HUST), Wuhan, China.
  • Juan Luo
  • Xu Chen
    School of Pharmaceutical Sciences, Shenzhen Campus of Sun Yat-sen University, Shenzhen 518107, China.
  • Jiyi Xie
    Department of Pharmacy, Union Hospital, Tongji Medical College, Huazhong University of Science & Technology (HUST), Wuhan, China.
  • Cong Wang
    Department of Vascular Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
  • Hanxiang Wang
    Department of Computer Science and Engineering, Sejong University, Seoul 143-747(05006), Korea.
  • Qi Yuan
  • Shijun Li
    Key Laboratory of Agricultural Animal Genetics, Breeding and Reproduction, Ministry of Education, Huazhong Agricultural University, Wuhan, Hubei Province, 430070 China. Electronic address: lishijun@mail.hzau.edu.cn.
  • Yu Zhang
    College of Marine Electrical Engineering, Dalian Maritime University, Dalian, China.
  • Jianli Hu
    Cancer Center, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
  • Chen Shi
    Sino-Dutch R&D Centre for Future Wastewater Treatment Technologies, Key Laboratory of Urban Stormwater System and Water Environment, Beijing University of Civil Engineering and Architecture Beijing 100044 China xdhao@hotmail.com.

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