Federated Random Forests can improve local performance of predictive models for various healthcare applications.

Hauschild, Anne-Christin; Lemanczyk, Marta; Matschinske, Julian; Frisch, Tobias; Zolotareva, Olga; Holzinger, Andreas; Baumbach, Jan; Heider, Dominik

Hauschild, Anne-Christin; Lemanczyk, Marta; Matschinske, Julian; Frisch, Tobias; Zolotareva, Olga; Holzinger, Andreas; Baumbach, Jan; Heider, Dominik.

Afiliação

Hauschild AC; Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Lemanczyk M; Department of Mathematics and Computer Science, University of Marburg, Marburg, Germany.
Matschinske J; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Germany.
Frisch T; Computational Systems Biology, University of Hamburg, Hamburg, Germany.
Zolotareva O; Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark.
Holzinger A; TUM School of Life Sciences Weihenstephan, Technical University of Munich, Freising-Weihenstephan, Germany.
Baumbach J; Institut für Medizinische Informatik, Statistik und Dokumentation, Medizinische Universität Graz, Graz, Austria.
Heider D; Computational Systems Biology, University of Hamburg, Hamburg, Germany.

Bioinformatics ; 38(8): 2278-2286, 2022 04 12.

Article em En | MEDLINE | ID: mdl-35139148

Assuntos

Privacidade; Algoritmo Florestas Aleatórias; Aprendizado de Máquina; Medicina de Precisão; Atenção à Saúde

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Privacidade / Algoritmo Florestas Aleatórias Tipo de estudo: Clinical_trials / Prognostic_studies / Risk_factors_studies Idioma: En Revista: Bioinformatics Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article País de afiliação: Alemanha

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