Towards interpretable, medically grounded, EMR-based risk prediction models.

Twick, Isabell; Zahavi, Guy; Benvenisti, Haggai; Rubinstein, Ronya; Woods, Michael S; Berkenstadt, Haim; Nissan, Aviram; Hosgor, Enes; Assaf, Dan

Twick, Isabell; Zahavi, Guy; Benvenisti, Haggai; Rubinstein, Ronya; Woods, Michael S; Berkenstadt, Haim; Nissan, Aviram; Hosgor, Enes; Assaf, Dan.

Afiliação

Twick I; Caresyntax GmbH, Komturstraße 18A, 12099, Berlin, Germany. isabell.twick@caresyntax.com.
Zahavi G; Department of Anesthesiology, The Chaim Sheba Medical Center, Ramat Gan, Israel.
Benvenisti H; Department of General and Oncological Surgery - Surgery C, The Chaim Sheba Medical Center, Ramat Gan, Israel.
Rubinstein R; Caresyntax GmbH, Komturstraße 18A, 12099, Berlin, Germany.
Woods MS; Caresyntax GmbH, Komturstraße 18A, 12099, Berlin, Germany.
Berkenstadt H; Department of Anesthesiology, The Chaim Sheba Medical Center, Ramat Gan, Israel.
Nissan A; Department of General and Oncological Surgery - Surgery C, The Chaim Sheba Medical Center, Ramat Gan, Israel.
Hosgor E; Caresyntax GmbH, Komturstraße 18A, 12099, Berlin, Germany.
Assaf D; Department of General and Oncological Surgery - Surgery C, The Chaim Sheba Medical Center, Ramat Gan, Israel.

Sci Rep ; 12(1): 9990, 2022 06 15.

Article em En | MEDLINE | ID: mdl-35705550

Assuntos

Registros Eletrônicos de Saúde; Aprendizado de Máquina; Humanos; Complicações Pós-Operatórias/etiologia; Medição de Risco; Fatores de Risco

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Registros Eletrônicos de Saúde / Aprendizado de Máquina Tipo de estudo: Etiology_studies / Prognostic_studies / Risk_factors_studies Limite: Humans Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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