Confederated learning in healthcare: Training machine learning models using disconnected data separated by individual, data type and identity for Large-Scale health system Intelligence.

Liu, Dianbo; Fox, Kathe; Weber, Griffin; Miller, Tim

Liu, Dianbo; Fox, Kathe; Weber, Griffin; Miller, Tim.

Afiliación

Liu D; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States; Department of Pediatrics, Harvard Medical School, Boston, MA, United States; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States; Computer Science & Artificial
Fox K; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States; Aetna, CVS Health, Boston, MA, United States.
Weber G; Department of Biomedical Informatics, Harvard Medical School, Boston, MA, United States.
Miller T; Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, United States; Department of Pediatrics, Harvard Medical School, Boston, MA, United States. Electronic address: Timothy.Miller@childrens.harvard.edu.

J Biomed Inform ; 134: 104151, 2022 10.

Article en En | MEDLINE | ID: mdl-35872264

Asunto(s)

Atención a la Salud; Aprendizaje Automático; Humanos; Inteligencia; Privacidad; Curva ROC

Palabras clave

Confederated machine learning; Diagnosis; Disease prediction; Federated machine learning; Healthcare insurance claims; Lab results; Medication

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Texto completo: 1 Bases de datos: MEDLINE Asunto principal: Atención a la Salud / Aprendizaje Automático Tipo de estudio: Prognostic_studies Límite: Humans Idioma: En Revista: J Biomed Inform Asunto de la revista: INFORMATICA MEDICA Año: 2022 Tipo del documento: Article

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