用机器学习预测术后疼痛和阿片类药物使用,应用于长度电子健康记录和可穿戴数据
Nidhi Soley1,2, Traci J Speed3, Anping Xie4,5
1Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, United States.
Applied clinical informatics
|May 7, 2024
概括
使用电子健康记录和可穿戴设备,可以预测术后疼痛和慢性阿片类药物使用. 这种方法可以个性化疼痛管理,减少阿片类药物依赖.
科学领域:
- 在医疗保健中的数据科学.
- 医学中的预测分析.
- 数字健康技术数字健康技术
背景情况:
- 对急性术后疼痛的有效管理对于患者的康复至关重要.
- 尽量减少慢性阿片类药物使用对于长期患者健康至关重要.
- 过度使用和依赖阿片类药物对患者构成重大风险.
研究的目的:
- 探索使用手术前电子健康记录 (EHR) 和可穿戴设备数据.
- 开发用于预测术后急性疼痛的机器学习模型.
- 预测手术后慢性阿片类药物使用的风险.
主要方法:
- 利用了"我们所有人"研究计划中大约347名参与者的数据.
- 开发并比较了四种机器学习模型:逻辑回归,随机森林,极端梯度增强和堆叠组合.
- 采用沙普利增量解释 (SHAP) 技术来识别关键预测因素.
主要成果:
- 堆叠组合模型在预测急性疼痛 (0.68) 和慢性阿片类药物使用 (0.89) 中表现出高准确性.
- 可穿戴设备数据对预测急性疼痛和慢性阿片类药物使用作出了重大贡献.
- 预测严重急性术后疼痛的曲线下的面积为0.88.
结论:
- 机器学习模型可以准确预测术后疼痛和慢性阿片类药物使用.
- 通过SHAP分析识别个体风险因素,可以制定量身定制的疼痛管理策略.
- 术前预测可以减轻与阿片类药物过度使用和依赖相关的风险,促进更安全的疼痛控制.
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