预测手术后持续使用阿片类药物:一种机器学习方法
Natalie B Baxter1, Allister Z Ho2, Jacqueline N Byrd3,4
1University of Michigan Medical School.
Plastic and reconstructive surgery
|September 29, 2023
概括
机器学习模型可以预测手术后持续使用阿片类药物. 密歇根基因组学倡议模型显示出强大的预测性能,有助于早期干预阿片类药物成预防.
科学领域:
- 整形外科手术 整形外科手术
- 数据科学数据科学数据科学
- 药理学 药理学是指药理学的学科.
背景情况:
- 持续使用阿片类药物是手术后的一个重大问题.
- 识别有风险的患者对于预防阿片类药物成至关重要.
研究的目的:
- 评估用于预测手术患者持续使用阿片类药物的机器学习算法.
- 为了比较不同机器学习模型的性能.
主要方法:
- 使用一般手术和手术数据集训练了两个算法 (密歇根基因组学倡议和索赔模型).
- 模型在2015-2018年手术患者数据 (n=889) 上进行了测试.
- 模型歧视是用接收器操作曲线 (AUC) 下的面积来衡量的.
主要成果:
- 密歇根基因组学倡议模型的AUC为0.84 (手术数据) 和0.85 (全队列).
- 索赔模型的表现较低,AUC为0.69 (手术数据) 和0.52 (阿片类药物未使用队列).
- 21%的手术患者持续使用阿片类药物.
结论:
- 机器学习,特别是密歇根基因组学倡议模型,在识别手术后患有持续阿片类药物使用风险的患者方面表现有前途.
- 早期识别可以促进预防阿片类药物成的干预措施.
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