ArticleScientific reports2021
Identifying and characterizing high-risk clusters in a heterogeneous ICU population with deep embedded clustering.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
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- Location and amount of joint involvement differentiates rheumatoid arthritis into different clinical subsets.NPJ digital medicine · 2025Article
- Identifying distinct clusters of ICU survivors by integrating demographic, pre-admission quality of life, and clinical data: a large prospective cohort study.Intensive care medicine · 2025Article
- Identifying and characterizing suicide decedent subtypes using deep embedded clustering.Scientific reports · 2025Article
- Deep representation learning for clustering longitudinal survival data from electronic health records.Nature communications · 2025Article
- Monitoring peripheral blood data supports the prediction of immunotherapy response in advanced non-small cell lung cancer based on real-world data.Cancer immunology, immunotherapy : CII · 2025Article
- Development of interpretable machine learning models to predict in-hospital prognosis of acute heart failure patients.ESC heart failure · 2024Article
- Deep behavioural representation learning reveals risk profiles for malignant ventricular arrhythmias.NPJ digital medicine · 2024Article
- Democratizing cheminformatics: interpretable chemical grouping using an automated KNIME workflow.Journal of cheminformatics · 2024Article
- A robust clustering strategy for stratification unveils unique patient subgroups in acutely decompensated cirrhosis.Journal of translational medicine · 2024Article
- Deep embedded clustering generalisability and adaptation for integrating mixed datatypes: two critical care cohorts.Scientific reports · 2024Article
- Explainable hierarchical clustering for patient subtyping and risk prediction.Experimental biology and medicine (Maywood, N.J.) · 2023Article
- Identifying individuals with intellectual disability who access mental health support and are at high risk for adverse clinical outcomes: cohort study.BJPsych open · 2023Article
- Clustering analysis of geriatric and acute characteristics in a cohort of very old patients on admission to ICU.Intensive care medicine · 2022Article
- Tailoring treatments to older people in intensive care. A way forward.Intensive care medicine · 2022Article
- Patient and hospital characteristics predict prolonged emergency department length of stay and in-hospital mortality: a nationwide analysis in Korea.BMC emergency medicine · 2022Article
- Interpretable Machine Learning for Inpatient COVID-19 Mortality Risk Assessments: Diabetes Mellitus Exclusive Interplay.Sensors (Basel, Switzerland) · 2022Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
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 was assessed using SHapley Additive exPlanations values. We found that deep embedded clustering yielded better results compared to the traditional clustering algorithms. The best cluster configuration was achieved for 6 clusters. All clusters were clinically recognizable, and differed in in-ICU, 30-day, and 90-day mortality, as well as incidence of acute kidney injury. We identified two high mortality risk clusters with at least 60%, 40%, and 30% increased. ICU, 30-day and 90-day mortality, and a low risk cluster with 25-56% lower mortality risk. This machine learning methodology combining deep embedded clustering and variable importance analysis, which we made publicly available, is a possible solution to challenges previously encountered by clustering analyses in heterogeneous patient populations and may help improve the characterization of risk groups in critical care.
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