Reinforced risk prediction with budget constraint using irregularly measured data from electronic health records

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.

未得到控制的糖化血红蛋白（glycated hemoglobin, HbA1c）水平与复杂糖尿病患者的不良事件密切相关。此类不良事件不仅会给受累患者带来严重的健康风险，还会造成高昂的经济成本。因此，一款能够识别高危患者以指导预防性治疗的高质量预测模型，有望在改善患者预后的同时降低医疗开支。由于预测风险所需的生物标志物信息获取成本高昂且操作繁琐，理想的预测模型应仅采集每位患者的必要信息即可实现精准预测。本研究提出一种序贯预测模型，该模型利用逐步积累的患者纵向数据，将患者划分为高危、低危或不确定三类：其中高危患者推荐接受预防性治疗，低危患者建议采用标准护理方案，而不确定类患者则需持续监测，直至明确其高危或低危属性。本研究利用美国医疗保险（Medicare）的理赔与参保档案，并结合患者电子健康档案（Electronic Health Records, EHR）数据构建该模型。所提出的模型采用函数主成分分析处理带有噪声的纵向数据，并通过加权方法应对数据缺失与抽样偏差问题。在一系列模拟实验以及针对复杂糖尿病患者数据的实际应用中，该方法相较其他同类方法展现出更高的预测精度与更低的成本开销。