Identifying Distinct Subgroups of Intensive Care Unit Patients: a Machine Learning Approach
By K. Vranas, Jeffrey K. Jopling, T. Sweeney, Meghan C. Ramsey, Arnold Milstein, C. Slatore, G. Escobar, V. Liu. 2017
82 citations recorded by the source corpus.
Objectives: Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients’ shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts. Design: We performed clustering analysis using data from patients’ hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population. Setting: Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members. Patients: ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals. Interventions: None. Measurements and Main Results: We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables. Conclusions: A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients’ shared needs.
Subfields: Artificial Intelligence, Critical Care and Intensive Care Medicine, Epidemiology
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
In Epidemiology
Citations in the corpus
- An overview of deep learning in medical imaging focusing on MRI (2018, background): “seizures [17] and cardiac arrests [18], to computer-aided detection [19] and diagnosis [20] supporting clinical decision making and survival analysis [21], to drug discovery [22] and as an aid in therapy selection and pharmacogenomics [23], to increased operational efficiency [24], stratified care d”
- eDoctor: machine learning and the future of medicine (2018, methodology): “…patient variables using an unsupervised algorithm (clustering), they were able to identify patient subgroups that had significantly different clinical courses despite similar diagnoses and when applying the predictive clusters to a separate, unseen data set, the predictive capability persisted [21].”