A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
A Naive Bayes machine learning approach to risk prediction using censored, time-to-event data.
复制标题
使用经过审查的，事件时间的数据，一种天真的贝叶斯机器学习方法来预测风险预测。
DOI：
10.1002/sim.6526
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发表时间：
2015-09-20
影响因子：
2
通讯作者：
O'Connor PJ
中科院分区：
文献类型：
--
作者：
Wolfson J;Bandyopadhyay S;Elidrisi M;Vazquez-Benitez G;Vock DM;Musgrove D;Adomavicius G;Johnson PE;O'Connor PJ
预测个体经历未来临床结果的风险是一项统计任务，对执业临床医生和公共卫生专家都具有重要意义。电子健康记录（EHR）等现代观察性数据库为传统上用于构建风险模型的纵向队列研究提供了一种替代方案，带来了机遇和挑战。大样本量和详细的协变量历史使得能够使用复杂的机器学习技术来揭示复杂的关联和相互作用，但观察数据库通常是“混乱的”，具有高水平的缺失数据和不完整的患者随访。在本文中，我们提出了一种适应著名的朴素贝叶斯（NB）机器学习方法的时间到事件的结果受到审查。我们比较了我们的方法的预测性能的考克斯比例风险模型，这是常用的风险预测在医疗保健人群，并说明其应用于预测心血管疾病的风险，使用EHR数据集从一个大型中西部综合医疗保健系统。
Predicting an individual’s risk of experiencing a future clinical outcome is a statistical task with important consequences for both practicing clinicians and public health experts. Modern observational databases such as electronic health records (EHRs) provide an alternative to the longitudinal cohort studies traditionally used to construct risk models, bringing with them both opportunities and challenges. Large sample sizes and detailed covariate histories enable the use of sophisticated machine learning techniques to uncover complex associations and interactions, but observational databases are often “messy,” with high levels of missing data and incomplete patient follow-up. In this paper, we propose an adaptation of the well-known Naive Bayes (NB) machine learning approach to time-to-event outcomes subject to censoring. We compare the predictive performance of our method to the Cox proportional hazards model which is commonly used for risk prediction in healthcare populations, and illustrate its application to prediction of cardiovascular risk using an EHR dataset from a large Midwest integrated healthcare system.