Predictive Modeling using Intensive Care Unit Data: Considerations for Data Pre-processing and Analysis

Abstract

The vast quantities of data generated and collected in the Intensive Care Unit (ICU) have given rise to large retrospective datasets that are frequently used for observational studies. The temporal nature and fine granularity of much of the data collected in the ICU enable the pursuit of predictive modeling, an increasingly common topic in ICU literature. Since patient conditions can rapidly change in the ICU, predicting the onset of events that are indicative of deteriorating patient state has potential clinical utility. However, the development of predictive modeling applications using ICU data requires a number of considerations to maximize prospective performance and clinical utility. In this study, we discuss the challenges encountered and considerations taken by using the prediction of acute hypotensive episodes as an example.