Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering.

ABSTRACT: Critically ill patients constitute a highly heterogeneous population, with seemingly distinct patients having similar outcomes, and patients with the same admission diagnosis having opposite clinical trajectories. We aimed to develop a machine learning methodology that identifies and provides better characterization of patient clusters at high risk of mortality and kidney injury. We analysed prospectively collected data including co-morbidities, clinical examination, and laboratory parameters from a minimally-selected population of 743 patients admitted to the ICU of a Dutch hospital between 2015 and 2017. We compared four clustering methodologies and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership

SUBMITTER: Castela Forte J 

PROVIDER: S-EPMC8187398 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature