Population scale latent space cohort matching for the improved use and exploration of observational trial data.

Gologorsky, Rachel; Somani, Sulaiman S; Neifert, Sean N; Valliani, Aly A; Link, Katherine E; Chen, Viola J; Costa, Anthony B; Oermann, Eric K

Gologorsky, Rachel; Somani, Sulaiman S; Neifert, Sean N; Valliani, Aly A; Link, Katherine E; Chen, Viola J; Costa, Anthony B; Oermann, Eric K.

Afiliación

Gologorsky R; Department of Medicine, Icahn School of Medicine, New York, NY 10028, USA.
Somani SS; Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
Neifert SN; Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY 10016, USA.
Valliani AA; Department of Medicine, Icahn School of Medicine, New York, NY 10028, USA.
Link KE; Department of Medicine, Icahn School of Medicine, New York, NY 10028, USA.
Chen VJ; Oncology Early development, Merck & Co., Inc, Kenilworth, NJ 07033, USA.
Costa AB; NVIDIA, Santa Clara, CA 95051, USA.
Oermann EK; Department of Neurosurgery, NYU Grossman School of Medicine, New York, NY 10016, USA.

Math Biosci Eng ; 19(7): 6795-6813, 2022 05 05.

Article en En | MEDLINE | ID: mdl-35730283

Asunto(s)

Proyectos de Investigación; Estudios de Cohortes; Humanos; Puntaje de Propensión

Palabras clave

artificial intelligence; autoencoders; cohort matching; data visualization; deep learning; manifold learning

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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Proyectos de Investigación Tipo de estudio: Clinical_trials / Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Math Biosci Eng Año: 2022 Tipo del documento: Article

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