Department of Decision, Operations & Information Technologies, Robert H. Smith School of Business, University of Maryland, College Park, MD, USA.
Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA.
J Healthc Eng. 2018 Mar 18;2018:7174803. doi: 10.1155/2018/7174803. eCollection 2018.
Widespread adoption of electronic health records (EHR) and objectives for meaningful use have increased opportunities for data-driven predictive applications in healthcare. These decision support applications are often fueled by large-scale, heterogeneous, and multilevel (i.e., defined at hierarchical levels of specificity) patient data that challenge the development of predictive models. Our objective is to develop and evaluate an approach for optimally specifying multilevel patient data for prediction problems. We present a general evolutionary computational framework to optimally specify multilevel data to predict individual patient outcomes. We evaluate this method for both flattening (single level) and retaining the hierarchical predictor structure (multiple levels) using data collected to predict critical outcomes for emergency department patients across five populations. We find that the performance of both the flattened and hierarchical predictor structures in predicting critical outcomes for emergency department patients improve upon the baseline models for which only a single level of predictor-either more general or more specific-is used ( < 0.001). Our framework for optimizing the specificity of multilevel data improves upon more traditional single-level predictor structures and can readily be adapted to similar problems in healthcare and other domains.
电子健康记录（EHR）的广泛采用和有意义使用的目标增加了数据驱动的预测应用在医疗保健中的机会。这些决策支持应用程序通常由大规模、异构和多层次（即在特定层次定义）的患者数据驱动，这给预测模型的开发带来了挑战。我们的目标是开发和评估一种最佳指定多层次患者数据用于预测问题的方法。我们提出了一种通用的进化计算框架，用于最优地指定多层次数据以预测个体患者的结果。我们使用收集的数据来评估该方法，以预测五个群体的急诊科患者的关键结局，包括对平坦化（单水平）和保留分层预测器结构（多水平）的评估。我们发现，无论是平坦化还是分层预测器结构，在预测急诊科患者的关键结局方面，都优于仅使用单一水平预测器（更一般或更具体）的基线模型（<0.001）。我们用于优化多层次数据特异性的框架优于更传统的单水平预测器结构，可以很容易地应用于医疗保健和其他领域的类似问题。
