经审查的事件时间数据的数据挖掘：用于从电子健康记录数据预测心血管风险的贝叶斯网络模型
Data mining for censored time-to-event data: A Bayesian network model for predicting cardiovascular risk from electronic health record data
摘要：
基于个体患者特征预测心血管事件风险的模型是管理患者护理的重要工具。大多数当前和常用的风险预测模型都是根据精心挑选的流行病学队列建立的。然而，这些队列的同质性和有限的规模限制了这些风险模型对其他人群的预测能力和普遍性。来自大型医疗保健系统的电子健康数据（EHD）提供了对关于大型、异质和同期患者群体的数据的访问。EHD的独特特征和挑战，包括风险因素信息缺失、风险因素与心血管事件结局之间的非线性关系以及不同患者亚组的不同影响，需要新的机器学习方法来开发风险模型。在本文中，我们提出了一种基于贝叶斯网络的机器学习方法，该方法在EHD上训练以预测五年内发生心血管事件的概率。在此类数据中，由于退出和不完整随访，事件时间被右删失，因此某些个体的事件状态可能未知。由于许多传统的数据挖掘方法不太适合这样的数据，我们描述了如何修改建模和评估技术，以占审查观察时间。我们表明，我们的方法可以导致更好的预测性能比考克斯比例风险模型（即，基于回归的方法，通常用于删失的至事件发生时间数据）或贝叶斯网络（使用{ \em{ad hoc} }方法进行右删失）。我们的技术是由美国中西部一个大型医疗保健系统的数据驱动和说明的。
英文摘要：
Models for predicting the risk of cardiovascular events based on individual patient characteristics are important tools for managing patient care. Most current and commonly used risk prediction models have been built from carefully selected epidemiological cohorts. However, the homogeneity and limited size of such cohorts restricts the predictive power and generalizability of these risk models to other populations. Electronic health data (EHD) from large health care systems provide access to data on large, heterogeneous, and contemporaneous patient populations. The unique features and challenges of EHD, including missing risk factor information, non-linear relationships between risk factors and cardiovascular event outcomes, and differing effects from different patient subgroups, demand novel machine learning approaches to risk model development. In this paper, we present a machine learning approach based on Bayesian networks trained on EHD to predict the probability of having a cardiovascular event within five years. In such data, event status may be unknown for some individuals as the event time is right-censored due to disenrollment and incomplete follow-up. Since many traditional data mining methods are not well-suited for such data, we describe how to modify both modelling and assessment techniques to account for censored observation times. We show that our approach can lead to better predictive performance than the Cox proportional hazards model (i.e., a regression-based approach commonly used for censored, time-to-event data) or a Bayesian network with {\em{ad hoc}} approaches to right-censoring. Our techniques are motivated by and illustrated on data from a large U.S. Midwestern health care system.
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