A machine learning approach to predict postoperative sleep disturbance after total knee arthroplasty: a comparative study of multiple algorithms
The study developed a robust and interpretable ML model for the early prediction of PSD following TKA. This model can aid in preoperative risk stratification, facilitating personalized management strategies to improve postoperative outcomes. Further validation in larger cohorts and diverse settings is necessary to enhance its broader clinical applicability.
Authors
- Chunlei Wu (ORCID: https://orcid.org/0000-0002-2629-6124)
- Lei Wang (ORCID: https://orcid.org/0009-0008-0206-0222)
- Sen He (ORCID: https://orcid.org/0000-0002-5204-8976)
- Yixiang Zhang (ORCID: https://orcid.org/0000-0003-2638-1668)
- Haoliang Li (ORCID: https://orcid.org/0000-0002-8723-8112)
- Jun Liu (ORCID: https://orcid.org/0000-0002-9001-9498)
- Tao Yang (ORCID: https://orcid.org/0000-0001-6375-3622)
- Xiaoquan Wang (ORCID: https://orcid.org/0009-0004-1947-6233)
Institutions
- Logistics University of People's Armed Police Force (CN)
- Tianjin Hospital (CN)
- Tianjin Beichen Hospital (CN)
- Pingjin Hospital (CN)
- Tianjin Medical University (CN)
Publication Details
- Journal
- Frontiers in Medicine
- Published
- 2025-11-05
- DOI
- https://doi.org/10.3389/fmed.2025.1699842
- Citations
- 1
- Primary Topic
- Total Knee Arthroplasty Outcomes
- Type
- article
- Field-Weighted Citation Impact
- 2.01