Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study.

Boussina, Aaron; Wardi, Gabriel; Shashikumar, Supreeth Prajwal; Malhotra, Atul; Zheng, Kai; Nemati, Shamim

Boussina, Aaron; Wardi, Gabriel; Shashikumar, Supreeth Prajwal; Malhotra, Atul; Zheng, Kai; Nemati, Shamim.

Affiliation

Boussina A; Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, United States.
Wardi G; Department of Emergency Medicine, University of California San Diego, San Diego, CA, United States.
Shashikumar SP; Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, San Diego, CA, United States.
Malhotra A; Division of Biomedical Informatics, University of California, San Diego, La Jolla, CA, United States.
Zheng K; Division of Pulmonary, Critical Care and Sleep Medicine, University of California San Diego, San Diego, CA, United States.
Nemati S; Department of Informatics, University of California, Irvine, Irvine, CA, United States.

J Med Internet Res ; 25: e45614, 2023 06 23.

Article in En | MEDLINE | ID: mdl-37351927

Subject(s)
Key words

artificial intelligence; clinical phenotype; clinical phenotyping; disease progression; emergency; emergency service, hospital; infection; machine learning; phenotype; sepsis; transition model; transition modeling

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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Sepsis Type of study: Diagnostic_studies / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Limits: Humans Language: En Journal: J Med Internet Res Journal subject: INFORMATICA MEDICA Year: 2023 Document type: Article Affiliation country: Country of publication:

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