Predicting mortality for patients in critical care : a univariate flagging approach

Abstract

Predicting outcomes for critically ill patients is a topic of considerable interest. The most widely used models utilize data from early in a patient's stay to predict risk of death. While research has shown that use of daily information, including trends in key variables, can improve predictions of patient prognosis, this problem is challenging as the number of variables that must be considered is large and increasingly complex modeling techniques are required. The objective of this thesis is to build a mortality prediction system that improves upon current approaches. We aim to do this in two ways: 1. By incorporating a wider range of variables, including time-dependent features 2. By exploring different predictive modeling techniques beyond standard regression We identify three promising approaches: a random forest model, a best subset regression containing just five variables, and a novel approach called the Univariate Flagging Algorithm (UFA). In this thesis, we show that all three methods significantly outperform a widely-used mortality prediction approach, the Sequential Organ Failure Assessment (SOFA) score. However, we assert that UFA in particular is well-suited for predicting mortality in critical care. It can detect optimal cut-points in data, easily scales to a large number of variables, is easy to interpret, is capable of predicting rare events, and is robust to noise and missing data. As such, we believe it is a valuable step toward individual patient survival estimates.