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向非特异性慢性腰部疼痛的数据驱动生物心理社会分类:一个试点研究

Scott D Tagliaferri^1,2,3, Patrick J Owen⁴, Clint T Miller⁴

¹Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences, Deakin University, Geelong, Australia. scott.tagliaferri@unimelb.edu.au.

Scientific reports

|August 12, 2023

概括

这项研究使用机器学习将慢性腰部疼痛 (CLBP) 患者根据心理社会因素分为子组. 这种数据驱动的方法为管理CLBP提供了新的见解.

科学领域:

生物医学工程生物医学工程
临床心理学临床心理学
数据科学数据科学数据科学

背景情况:

慢性腰部疼痛 (CLBP) 管理缺乏基于多维数据的子组分类.
以前的系统审查表明,基于数据的CLBP亚型存在差距.
包括神经系统,脊髓组织和心理社会因素在内的多维因素对于理解CLBP至关重要.

研究的目的:

用数据驱动方法将非特异性慢性腰部疼痛 (CLBP) 患者分为不同的子组.
调查机器学习模型对CLBP分类的有用性.
确定导致CLBP异质性的关键因素.

主要方法:

展望横截面研究涉及21名CLBP患者和21名无疼痛对照.
收集神经系统,腰椎组织和心理社会数据.
应用缩小维度,模糊c-means集群和机器学习模型 (SVM,k-NN,naive Bayes,随机森林) 来进行分类.

主要成果:

四个因素 (认知功能,抑郁症状,自我效能,焦虑症状) 和两个群体 (正常与受损的心理社会特征) 最佳分类的CLBP参与者.
在CLBP组中,分类错误率从4.2%到14.2%不等.
当包括无疼痛对照组时,错误率增加到24.2%-37.5%.

结论:

这项试点研究成功地将CLBP患者分为子组,主要由心理社会因素驱动.
它代表了CLBP的第一个基于数据的机器学习分类,使用了全面的多维数据.
建议采用更大的样本大小,以便在未来验证这些发现.

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