Predicting the Risk of Lumbar Prolapsed Disc: A Gene Signature-Based Machine Learning Analysis
The results of our study suggest that machine learning models based on pain-related gene signatures may identify patients at high risk of developing LPD with reasonably high accuracy. These prediction models could perhaps be integrated into clinical diagnostic tools to enhance early diagnosis and prevention.
Authors
- Fei Meng (ORCID: https://orcid.org/0000-0001-9225-040X)
- Stanley Sau Ching Wong (ORCID: https://orcid.org/0000-0002-8763-5687)
- Fengfeng Wang (ORCID: https://orcid.org/0000-0003-4959-4527)
Institutions
- Queen Mary Hospital (CN)
- University of Hong Kong (HK)
Publication Details
- Journal
- Pain and Therapy
- Published
- 2025-05-04
- DOI
- https://doi.org/10.1007/s40122-025-00744-4
- Primary Topic
- Spine and Intervertebral Disc Pathology
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Li Ka Shing Faculty of Medicine, University of Hong Kong