The use of clustering algorithms in critical care research to unravel patient heterogeneity
- 6 May 2019
- journal article
- editorial
- Published by Springer Science and Business Media LLC in Intensive Care Medicine
- Vol. 45 (7), 1025-1028
- https://doi.org/10.1007/s00134-019-05631-z
Abstract
No abstract availableThis publication has 13 references indexed in Scilit:
- Multiview Cluster Analysis Identifies Variable Corticosteroid Response Phenotypes in Severe AsthmaAmerican Journal of Respiratory and Critical Care Medicine, 2019
- Identification of Acute Kidney Injury Subphenotypes with Differing Molecular Signatures and Responses to Vasopressin TherapyAmerican Journal of Respiratory and Critical Care Medicine, 2019
- Cardiovascular clusters in septic shock combining clinical and echocardiographic parameters: a post hoc analysisIntensive Care Medicine, 2019
- Phenotypes in acute respiratory distress syndrome: moving towards precision medicineCurrent Opinion in Critical Care, 2019
- Latent class analysis of ARDS subphenotypes: a secondary analysis of the statins for acutely injured lungs from sepsis (SAILS) studyIntensive Care Medicine, 2018
- Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach*Critical Care Medicine, 2017
- Targeted use of growth mixture modeling: a learning perspectiveStatistics in Medicine, 2016
- Data clustering: 50 years beyond K-meansPattern Recognition Letters, 2009
- Computational cluster validation in post-genomic data analysisBioinformatics, 2005
- On clustering validation techniquesJournal of Intelligent Information Systems, 2000