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使用可解释的机器学习预测慢性疼痛的老年患者的脆弱性:一个横截面研究

Xiaoang Zhang¹, Yuping Liao¹, Daying Zhang²

¹School of Nursing, Jiangxi Medical College, Nanchang University, Nanchang, China; Department of Pain Medicine, the 1(st) affiliated hospital, Jiangxi Medical College, Nanchang University, Nanchang, China.

Geriatric nursing (New York, N.Y.)

|November 9, 2024

概括

识别慢性疼痛的老年人的脆弱性至关重要. 使用疼痛程度和抑郁症等因素的机器学习模型显示了早期脆弱性检测的高准确性.

科学领域:

老年学是一门学科.
计算医学是一种计算医学.
疼痛管理疼痛管理

背景情况:

脆弱性在经历慢性疼痛的老年人中普遍存在,增加了跌倒,残疾和认知能力下降的风险.
目前用于识别这一人口群体的脆弱性的方法是不够的.
早期发现脆弱性对于及时干预和改善健康结果至关重要.

研究的目的:

调查慢性疼痛的老年人的脆弱风险因素.
开发和评估机器学习模型,以准确识别脆弱性.
为了增强老年患者的临床决策持续性疼痛.

主要方法:

开发了九种不同的机器学习模型来预测脆弱性.
使用沙普利增量解释 (SHAP) 方法来解释模型的可解释性.
随机森林 (RF) 模型是根据性能指标选择的.

主要成果:

随机森林模型实现了高性能:准确度为0.822,精度为0.797,AUC为0.881.
关键预测变量包括年龄,BMI,教育,疼痛持续时间,疼痛部位数,疼痛水平,抑郁和日常生活活动 (ADL).
疼痛水平,抑郁和ADL被确定为RF模型中最重要的因素.

结论:

一个强大的机器学习模型,特别是随机森林,可以有效地识别慢性疼痛的老年人的脆弱性.
该模型依赖于临床和自我报告的数据,这有助于在医疗保健环境中的实际应用.
通过这种模型的早期识别可以导致有针对性的干预,促进健康的衰老和减少不良健康事件.

关键词:

慢性疼痛是一种慢性疼痛.年长的老人.脆弱性脆弱性脆弱性机器学习是机器学习.预测模型,SHAP 的预测模型.

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