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基于机器学习,将患者报告的结果与世卫组织功能,残疾和健康活动/参与类别的国际分类联系起来

Richard Habenicht¹, Elisabeth Fehrmann^1,2, Peter Blohm¹

¹Karl-Landsteiner-Institute of Outpatient Rehabilitation Research, 1230 Vienna, Austria.

Journal of clinical medicine

|September 9, 2023

概括

优化的机器学习方法准确地将患者报告的结果 (PROM) 与慢性腰部疼痛 (cLBP) 的国际功能,残疾和健康分类 (ICF) 联系起来. 最少一组15个PROM可以在不影响性能的情况下实现这一目标.

科学领域:

康复医学康复医学康复医学
医疗信息学医疗信息学
医疗保健中的机器学习

背景情况:

患者报告的结果措施 (PROMs) 对于评估医疗保健中的功能健康至关重要.
世界卫生组织 (WHO) 建议使用国际功能,残疾和健康分类 (ICF) 框架来分类患者的功能.
将PROM与ICF框架整合起来对于所有部门的综合健康评估至关重要.

研究的目的:

优化机器学习 (ML) 方法,将PROM数据自动链接到ICF慢性腰部疼痛 (cLBP) 类别.
确定所需的最小PROM集,以便在不降低性能的情况下进行准确的链接.
通过ML提高活动限制和参与限制的分类.

主要方法:

利用了806名cLBP患者的数据,这些患者完成了全面的PROM和ICF评估.
开发和完善随机森林 (RF) 方法,根据PROM数据对ICF活动和参与类别进行分类.
识别了关键PROM项目,并使用最小项目子集验证了链接性能.

主要成果:

新的射频方法比现有的ML链接技术 (ROC-AUC 0.73-0.81) 显示出更好的性能.
变量重要性分析表明,最少一组15-24个PROM可以保持链接性能.
优化的ML模型准确地预测了ICF核心类别中cLBP的限制和限制.

结论:

基于ML的优化方法可以准确预测cLBP患者的功能限制和参与限制.
将PROM数量减少到15项的最小集不会显著影响ICF链接的准确性.
这些发现支持在临床实践中高效准确地使用与ICF框架集成的PROM.

关键词:

功能国际分类的功能国际分类.腰部疼痛腰部疼痛腰部疼痛腰部疼痛机器学习是机器学习.患者报告的结果衡量患者报告的结果.随机的森林随机的森林

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