The potential for leveraging machine learning to filter medication alerts
The potential for leveraging machine learning to filter medication alerts
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DOI：
10.1093/jamia/ocab292
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发表时间：
2022-04-13
影响因子：
6.4
通讯作者：
Abdelrahman, Samir
中科院分区：
文献类型：
--
作者：
Liu, Siru;Kawamoto, Kensaku;Abdelrahman, Samir
目的 评估机器学习预测用户可能忽略的药物警报的潜力，并从用户的视图中智能地过滤掉这些警报。材料和方法我们通过文献确定了建议调节用户对药物警报的反应的特征（例如患者和提供者特征）；然后通过专家评审对这些功能进行完善。模型是使用基于规则的机器学习技术（逻辑回归、随机森林、支持向量机、神经网络和 LightGBM）开发的。我们收集了犹他大学健康中心 2019 年向用户显示的警报日志数据。我们力求在保持假阴性率的同时最大限度地提高精确度
Objective To evaluate the potential for machine learning to predict medication alerts that might be ignored by a user, and intelligently filter out those alerts from the user's view. Materials and Methods We identified features (eg, patient and provider characteristics) proposed to modulate user responses to medication alerts through the literature; these features were then refined through expert review. Models were developed using rule-based and machine learning techniques (logistic regression, random forest, support vector machine, neural network, and LightGBM). We collected log data on alerts shown to users throughout 2019 at University of Utah Health. We sought to maximize precision while maintaining a false-negative rate