Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records
Reinforced Risk Prediction With Budget Constraint Using Irregularly Measured Data From Electronic Health Records
复制标题
使用电子健康记录中不定期测量的数据在预算限制下强化风险预测
DOI：
10.1080/01621459.2021.1978467
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发表时间：
2023
影响因子：
3.7
通讯作者：
Zhao, Ying-Qi
中科院分区：
文献类型：
--
作者：
Pan, Yinghao;Laber, Eric B.;Smith, Maureen A.;Zhao, Ying-Qi
未控制的糖化血红蛋白(HbA1c)水平与复杂糖尿病患者的不良事件有关。这些不良事件给受影响的患者带来了严重的健康风险，并与巨大的经济成本有关。因此，高质量的预测模型可以识别高危患者，从而为预防性治疗提供信息，有可能在降低医疗成本的同时改善患者的预后。因为预测风险所需的生物标记物信息既昂贵又繁重，这样的模型最好只收集每个患者所需的信息，以便提供准确的预测。我们提出了一个序贯预测模型，该模型使用累积的患者纵向数据来将患者分类为：高风险、低风险或不确定。然后建议被归类为高风险的患者接受预防性治疗，而被归类为低风险的患者被建议接受标准护理。对被归类为不确定的患者进行监测，直到做出高风险或低风险的确定。我们使用Medicare的索赔和登记文件构建模型，并将其与患者电子健康记录(EHR)数据相链接。该模型使用函数主成分来适应有噪声的纵向数据，并使用加权来处理遗漏和采样偏差。在一系列模拟实验和对复杂糖尿病患者数据的应用中，该方法表现出比竞争方法更高的预测精度和更低的成本。这篇文章的补充材料可以在网上找到。
Uncontrolled glycated hemoglobin (HbA1c) levels are associated with adverse events among complex diabetic patients. These adverse events present serious health risks to affected patients and are associated with significant financial costs. Thus, a high-quality predictive model that could identify high-risk patients so as to inform preventative treatment has the potential to improve patient outcomes while reducing healthcare costs. Because the biomarker information needed to predict risk is costly and burdensome, it is desirable that such a model collect only as much information as is needed on each patient so as to render an accurate prediction. We propose a sequential predictive model that uses accumulating patient longitudinal data to classify patients as: high-risk, low-risk, or uncertain. Patients classified as high-risk are then recommended to receive preventative treatment and those classified as low-risk are recommended to standard care. Patients classified as uncertain are monitored until a high-risk or low-risk determination is made. We construct the model using claims and enrollment files from Medicare, linked with patient electronic health records (EHR) data. The proposed model uses functional principal components to accommodate noisy longitudinal data and weighting to deal with missingness and sampling bias. The proposed method demonstrates higher predictive accuracy and lower cost than competing methods in a series of simulation experiments and application to data on complex patients with diabetes. Supplementary materials for this article are available online.
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影响因子：
37.8
作者：
Wilson, PWF;D'Agostino, RB;Kannel, WB
通讯作者：
Kannel, WB
影响因子：
16.2
作者：
通讯作者：
--
影响因子：
4.2
作者：
C. Weisner;C. Weisner;G. Ray;J. Mertens;D. Satre;D. Satre;C. Moore
通讯作者：
C. Weisner;C. Weisner;G. Ray;J. Mertens;D. Satre;D. Satre;C. Moore
DOI：
--
发表时间：
2013-04
期刊：
--
影响因子：
--
作者：
K. Trapeznikov;Venkatesh Saligrama
通讯作者：
K. Trapeznikov;Venkatesh Saligrama
影响因子：
1.9
作者：
Goldsmith J;Greven S;Crainiceanu C
通讯作者：
Crainiceanu C