在社区药房使用深度学习和可解释性方法进行生活质量分析
María José Reyes-Medina1, María Del Pilar Carrera-González2,3, Vanesa Cantón-Habas1,2
1Department of Nursing, Pharmacology and Physiotherapy, Faculty of Medicine and Nursing, University of Córdoba, Córdoba, 14004, Spain.
JAMIA open
|February 4, 2026
概括
深度学习模型有效地确定了影响慢性病患者生活质量的关键因素. 疼痛,流动性和心理健康显著影响患者的结果,指导个性化护理.
科学领域:
- 临床信息学 临床信息学
- 医疗保健中的机器学习
- 患者生活质量研究研究 患者生活质量研究
背景情况:
- 慢性疾病对患者的生活质量造成重大负担.
- 社区药房为临床数据收集提供可访问的设置.
- 确定影响生活质量的关键因素对于个性化慢性疾病管理至关重要.
研究的目的:
- 开发和评估一个深度学习模型来处理来自社区药房的临床数据.
- 确定影响慢性病患者健康相关生活质量的关键变量.
- 利用全球和本地可解释性方法来解释模型的可解释性.
主要方法:
- 分析了来自347名慢性患者的数据,其中有257个变量.
- 五种预测模型的比较:梯度增强,随机森林,LightGBM和完全连接的神经网络 (FCNNs).
- 应用夏普利添加式解释 (SHAP) 对全球变量重要性和局部可解释模型-不可知解释 (LIME) 对局部解释.
主要成果:
- 一个完全连接的神经网络 (FCNN) 组合实现了最高的性能 (R2 = 0.511 ± 0.126).
- 可解释性分析确定了疼痛,运动限制,β阻断剂使用,焦虑/抑郁和日常生活困难作为关键影响变量.
- 深度学习模型成功地捕获了临床和心理社会变量之间的复杂关系.
结论:
- 深度学习模型与SHAP和LIME相结合,为个性化慢性疾病护理提供了临床可解释的见解.
- 社区药房是收集数据和应用预测模型的实际环境.
- 机器学习通过识别影响生活质量的关键因素,支持慢性疾病管理中的个性化决策.
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