预测使用行政数据对老年人使用二胺/Z药物处方的30天风险:预后机器学习方法
Vishal Sharma1, Tanya Joon2, Vinaykumar Kulkarni2
12-040 Li Ka Shing Center for Health Research Innovation, School of Public Health, University of Alberta, Edmonton, Alberta T6G 2E1, Canada.
International journal of medical informatics
|August 17, 2023
概括
使用行政数据的机器学习模型可以估计老年人使用二胺 (BZRA) 的风险. 然而,对于卫生监管机构来说,预测最有信息的是每年,而不是每天或每季度.
科学领域:
- 医疗信息学 医疗信息学
- 药物监督 药物监督 药物监督
- 医疗保健中的机器学习
背景情况:
- bensodiazepine (BZRA) 的使用在老年人中很常见,带有不良结果的风险.
- 行政卫生数据为风险预测模型提供了一个潜在的来源.
- 卫生部门和监管机构需要工具来监测和管理药物风险.
研究的目的:
- 开发和验证一种机器学习 (ML) 模型,以预测在老年人中使用二 (BZRA) 后的30天不良结果.
- 评估ML衍生风险预测对卫生部门和监管机构的有用性.
主要方法:
- 在加拿大阿尔伯塔省使用2018年行政数据开发了一种ML模型,用于65岁及以上的个人.
- 该模型预测了在BZRA发放后30天内住院,急诊室访问或死亡的风险.
- 使用XGBoost对2019年的数据进行验证,评估歧视和校准;每天和每季度进行预测模拟.
主要成果:
- 该研究包括65,063名参与者和333,333个BZRA处方.
- 在XGBoost模型中,C-统计值达到0.75.
- 虽然高风险预测 (前0.1百分位) 显示了90%的积极预测值,但每日和季度模拟产生了不信息的预测 (LR + < 10).
结论:
- 仅使用行政数据的机器学习模型对老年人二胺 (BZRA) 风险管理的及时监管干预可能具有有限的实用性.
- 年度风险分类可能比每日或季度预测更具信息性.
- 建议整合额外的数据源,以增强ML模型的预测能力和信息性,以帮助卫生监管机构.
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