Prediction and Inference with Missing Data in Patient Alert Systems

TL;DR

This paper introduces a Bayesian method for risk prediction in patient alert systems that effectively handles missing data and quantifies uncertainty, improving early detection of patient deterioration.

Contribution

The paper presents a novel Bayesian approach that models missing data explicitly and accounts for uncertainty, enhancing predictive accuracy in health monitoring.

Findings

Achieved excellent predictive performance in identifying deteriorating patients.

Effectively modeled missing data and uncertainty in risk predictions.

Handled missing not at random situations with improved flexibility.

Abstract

We describe the Bedside Patient Rescue (BPR) project, the goal of which is risk prediction of adverse events for non-ICU patients using ~200 variables (vitals, lab results, assessments, ...). 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.