Lost in Translation? From Conventional Scoring Tools to Modern Data-Driven Risk Assessment in Critical Care Medicine

Abstract

High-resolution, longitudinal health data became widely available in intensive care units in the past years. Patient risk assessment, however, is still primarily based on conventional scores that take into account only a few parameters taken at single time points, which frequently causes inaccurate predictions in the clinical practice. Likewise, the contribution of AI-approaches remains sparse, as current machine learning models are inherently difficult to deduce and even impressive results rarely contribute to disease understanding. This review focusses on the limitations of conventional risk scores, and on recent developments and challenges of novel, data-driven assessment tools.