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Updated: Jan 15, 2026

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation

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Published on: December 13, 2024

可解释的机器学习应用于低背部疼痛的生物电阻:分类和疼痛评分预测

Seungwan Jang¹, Seung Mo Yoo², Se Dong Min^1,3

¹Department of Software Convergence, Soonchunhyang University, Asan 31538, Republic of Korea.

Sensors (Basel, Switzerland)

|October 16, 2025

概括

需要针对腰部疼痛 (LBP) 的客观生物标志物. 通过可解释的机器学习分析的生物电阻参数 (BIP) 在识别LBP和估计疼痛强度方面表现有前途.

关键词:

这就是 SHAP SHAP 的意思.在XGBoost中使用.生物电阻的生物电阻.可以解释的机器学习腰部疼痛腰部疼痛腰部疼痛

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科学领域:

生物医学工程生物医学工程
计算医学是一种计算医学.
疼痛研究疼痛研究

背景情况:

腰部疼痛 (LBP) 是全球导致残疾的主要原因之一.
目前的LBP评估在很大程度上依赖于主观问卷.
目标生物标志物对于准确的LBP评估至关重要.

研究的目的:

调查生物电阻参数 (BIP) 对客观LBP评估的有用性.
应用可解释的机器学习 (ML) 模型用于LBP分类和疼痛强度预测.
探索BIP作为LBP的潜在客观生物标志物.

主要方法:

一项涉及83名参与者的横截面研究 (38名患有LBP,45名对照).
收集了腰部BIP和人口统计数据.
极端梯度增强 (XGBoost) 与SHapley添加式扩展 (SHAP) 用于ML分析.
进行了LBP与健康状况的分类和疼痛尺度 (VAS,ODI,RMDQ) 的回归.

主要成果:

ML分类器在区分LBP和健康个体方面取得了高准确性 (ROC-AUC = 0.996).
该模型在预测视觉模拟尺度 (VAS) 疼痛评分 (R2 = 0.70) 中表现出强的表现.
对于奥斯威斯特残疾指数 (ODI) 和罗兰-莫里斯残疾问卷 (RMDQ) 的预测准确度较低.

结论:

使用BIP的可解释的ML模型可以有效地区分LBP和健康组.
BIP分析显示了对LBP强度的客观估计的潜力.
这些发现表明BIP是对主观LBP评估的有价值的客观补充.