Hierarchical Representation of Complex Intervention Sequences for Automated Subgroup Analysis in Critical Care Settings

Abstract

Our understanding of the impact of interventions in critical care is limited by the lack of techniques that represent and analyze complex intervention spaces applied across heterogeneous patient populations.Existing work has mainly focused on selecting a few interventions and rep-resenting them as binary variables, resulting in over simplification of intervention representation.The goal of this study is to find effective representations of sequential interventions to support intervention effect analysis.To this end, we have developed Hi-RISE(Hierarchical Representation of Intervention Sequences),an approach that transforms and clusters sequential interventions into a la-tent space, with the resulting clusters used for heterogenous treatment effect analysis.We apply this approach to the MIMIC III dataset and identified intervention clusters and corresponding subpopulations with peculiar odds of 28-day mortality. Our approach may lead to a better understanding of the subgroup-level effects of sequential interventions and improve targeted intervention planning in critical care settings.