Abstract We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-intensive care unit patients using ∼100 variables (vitals, lab results, assessments, etc.). There are several missing predictor values for most patients, which in the health sciences is the norm, rather than the exception. A Bayesian approach is presented that addresses many of the shortcomings to standard approaches to missing predictors: (i) treatment of the uncertainty due to imputation is straight-forward in the Bayesian paradigm, (ii) the predictor distribution is flexibly modeled as an infinite normal mixture with latent variables to explicitly account for discrete predictors (i.e., as in multivariate probit regression models), and (iii) certain missing not at random situations can be handled effectively by allowing the indicator of missingness into the predictor distribution only to inform the distribution of the missing variables. The proposed approach also has the benefit of providing a distribution for the prediction, including the uncertainty inherent in the imputation. Therefore, we can ask questions such as: is it possible this individual is at high risk but we are missing too much information to know for sure? How much would we reduce the uncertainty in our risk prediction by obtaining a particular missing value? This approach is applied to the BPR problem resulting in excellent predictive capability to identify deteriorating patients. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

中文翻译：

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患者警报系统中缺失数据的预测和推理

摘要 我们描述了床边患者救援 (BPR) 项目，其目标是使用 100 个变量（生命体征、实验室结果、评估等）对非重症监护病房患者的不良事件进行风险预测。大多数患者有几个缺失的预测值，这在健康科学中是常态，而不是例外。提出了一种贝叶斯方法，它解决了缺失预测变量的标准方法的许多缺点：（i）在贝叶斯范式中，由于插补引起的不确定性的处理是直接的，（ii）预测变量分布被灵活地建模为无限正态与潜在变量混合以明确解释离散预测变量（即，如在多元概率回归模型中），(iii) 某些非随机情况下的缺失可以通过允许缺失指标进入预测变量分布仅通知缺失变量的分布来有效处理。所提出的方法还具有为预测提供分布的好处，包括插补中固有的不确定性。因此，我们可以提出以下问题：这个人是否有可能处于高风险中，但我们错过了太多信息而无法确定？通过获得特定的缺失值，我们会在多大程度上减少风险预测的不确定性？这种方法应用于 BPR 问题，从而具有出色的预测能力，可以识别病情恶化的患者。本文的补充材料，