Predicting Treatment Outcomes in Patients with Low Back Pain Using Gene Signature-Based Machine Learning Models
The study demonstrates the potential of using transcriptomic data from peripheral immune cells and machine learning models to predict treatment outcomes in patients with LBP. The identification of key genes and the high accuracy of certain models provide a basis for future personalized treatment strategies in LBP management. Visualizing gene importance with SHAP adds interpretability to the predictive models, enhancing their clinical relevance.
Authors
- Yin-Yu Shi
- Haibin Shang (ORCID: https://orcid.org/0000-0003-4129-2014)
- Zhan Hong-sheng (ORCID: https://orcid.org/0000-0002-3119-8596)
- Youzhi Lian
Institutions
- Shanghai University (CN)
- Shanghai Medical Information Center (CN)
- Shanghai University of Traditional Chinese Medicine (CN)
- Integrated Chinese Medicine (China) (HK)
- Shuguang Hospital (CN)
Publication Details
- Journal
- Pain and Therapy
- Published
- 2024-12-25
- DOI
- https://doi.org/10.1007/s40122-024-00700-8
- Citations
- 5
- Primary Topic
- Musculoskeletal pain and rehabilitation
- Type
- article
- Field-Weighted Citation Impact
- 2.24
Funders
- National Natural Science Foundation of China