ArticleComputational and mathematical methods in medicine2022
Logistic Model and Gradient Boosting Machine Model for Physical Therapy of Lumbar Disc Herniation.
Article in Computational and mathematical methods in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed, 6 citations in OpenAlex.
- A Gamified Pain Management Intervention for Adults With Chronic Pain in Mainland China: Single-Arm Pre-Post Pilot Study With Machine Learning Predictive Modeling.JMIR formative research · 2026Article
- Single-cell sequencing analysis and machine learning model reveal aberrant TIM-3 expression in microglia during Alzheimer's disease progression.Journal of translational medicine · 2026Article
- Machine learning-based prediction of short-term outcomes in aneurysmal subarachnoid hemorrhage: a multicenter study integrating clinical and inflammatory indicators.BMC medicine · 2025Article
- Construction of a logistic model and GBM model for infection after spinal canal resection of intraspinal tumors.American journal of translational research · 2025Article
- Recent advances and evolving strategies in the treatment of lumbar disc herniation.Frontiers in neurology · 2025Review
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: Physical therapy is a common clinical treatment for patients with lumbar disc herniation. The study is aimed at exploring the feasibility of mathematical expression and curative effect prediction of physical therapy in patients with lumbar disc herniation using a logistic model and gradient boosting machine (GBM). Methods: A total of 142 patients with lumbar disc herniation were treated with physical therapy. The pain was evaluated by the visual analogue scale (VAS) before each treatment. The logistic model was used to conduct a global regression analysis on patients with lumbar disc herniation. The final results of the whole course of treatment were predicted by the measured values of 2-9 times of treatment. The GBM model was used to predict and analyze the curative effect of physical therapy. Results: The mathematical expression ability of the logistic regression model for patients with lumbar disc herniation undergoing physical therapy was sufficient, and the global determination coefficient was 0.721. The results would be better for more than five measurements. The AUC of GBM mode logistic regression analysis was 0.936 and 0.883, and the prediction effect is statistically significant. Conclusion: Both the logistic and GBM model can fully express the changes in patients with lumbar disc herniation during physical therapy.
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