Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study
Unsupervised clustering applied to diverse, large-scale medical data offers a promising approach to the identification of sepsis phenotypes for epidemiological surveillance.
Authors
- Kiarash Riazi (ORCID: https://orcid.org/0000-0002-2291-7405)
- Hallie C. Prescott (ORCID: https://orcid.org/0000-0002-8442-6724)
- Allan Garland (ORCID: https://orcid.org/0000-0001-7129-936X)
- Bram Rochwerg (ORCID: https://orcid.org/0000-0002-8293-7061)
- Peter Dodek (ORCID: https://orcid.org/0000-0002-3314-4655)
- Jennifer Ziegler (ORCID: https://orcid.org/0000-0002-2139-3427)
- Bing Li (ORCID: https://orcid.org/0000-0003-3650-4166)
- Kednapa Thavorn (ORCID: https://orcid.org/0000-0003-4738-8447)
- Hude Quan (ORCID: https://orcid.org/0000-0002-7848-7256)
- Na Li (ORCID: https://orcid.org/0000-0002-4803-0984)
- Alain Gervais
- Jie Pan (ORCID: https://orcid.org/0000-0001-6398-1756)
Institutions
- Alberta Health Services (CA)
- Université de Sherbrooke (CA)
- University of British Columbia (CA)
- University of Ottawa (CA)
- University of Calgary (CA)
- University of Michigan (US)
- Ottawa Hospital (CA)
- Centre Intégré Universitaire de Santé et de Services Sociaux du Saguenay–Lac-Saint-Jean (CA)
- Alberta Health (CA)
- St. Paul's Hospital (CA)
- Centre Intégré Universitaire de Santé et de Services Sociaux du Centre-Sud-de-l'Île-de-Montréal (CA)
- Impact (CA)
- Ottawa Hospital Research Institute (CA)
- University of Manitoba (CA)
- McMaster University (CA)
Publication Details
- Journal
- Intensive Care Medicine Experimental
- Published
- 2025-03-20
- DOI
- https://doi.org/10.1186/s40635-025-00744-w
- Citations
- 8
- Primary Topic
- Sepsis Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 10.83
Funders
- Health Sciences Centre Foundation
- Canadian Institutes of Health Research