Introduction Despite extensive research, the goal of unravelling patient heterogeneity in critical care remains largely unattained. Combining clustering analysis of routinely collected high-frequency data with the identification of features driving cluster separation may constitute a step towards improving patient characterization. Methods In this study, we analysed prospectively collected data from 743 patients including co-morbidities, clinical examination, and laboratory parameters. We compared four clustering methodologies – deep embedded clustering (DEC), hierarchical clustering with and without dynamic time warping, and k-means – and trained a classifier to predict and validate cluster membership. The contribution of different variables to the predicted cluster membership was assessed using SHapley Additive exPlanations values.Results DEC yielded better results compared to the traditional clustering algorithms, with the best Jaccard and entropy scores being achieved for 6 clusters. These clusters were characterized as medium to high co-morbidity patients with respiratory pathology and sepsis (cluster 1), patients with primarily acute and chronic cardiac conditions and surgical admission (cluster 2), patients with diverse disease etiology and poor outcomes (cluster 3), low co-morbidity neurological, neurosurgical, and trauma patients (cluster 4), medium co-morbidity patients with cardio-respiratory problems, and neuro-trauma patients with longer length of stay (cluster 5), and patients with sepsis and respiratory infections (cluster 6). All clusters differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury, and two clusters were categorized as having higher mortality risk, and one cluster as lower mortality risk. Conclusions This machine learning methodology, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses, and may help unravel patient heterogeneity in critical care.