An artificial intelligence-based approach for identifying rare disease patients using retrospective electronic health records applied for Pompe disease.

Lin, Simon; Nateqi, Jama; Weingartner-Ortner, Rafael; Gruarin, Stefanie; Marling, Hannes; Pilgram, Vinzenz; Lagler, Florian B; Aigner, Elmar; Martin, Alistair G

Lin, Simon; Nateqi, Jama; Weingartner-Ortner, Rafael; Gruarin, Stefanie; Marling, Hannes; Pilgram, Vinzenz; Lagler, Florian B; Aigner, Elmar; Martin, Alistair G.

Afiliação

Lin S; Science Department, Symptoma GmbH, Vienna, Austria.
Nateqi J; Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria.
Weingartner-Ortner R; Science Department, Symptoma GmbH, Vienna, Austria.
Gruarin S; Department of Internal Medicine, Paracelsus Medical University, Salzburg, Austria.
Marling H; Science Department, Symptoma GmbH, Salzburg, Austria.
Pilgram V; Science Department, Symptoma GmbH, Salzburg, Austria.
Lagler FB; Science Department, Symptoma GmbH, Vienna, Austria.
Aigner E; Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria.
Martin AG; Medical and Information Technology - MIT, University Hospital Salzburg (SALK), Salzburg, Austria.

Front Neurol ; 14: 1108222, 2023.

Article em En | MEDLINE | ID: mdl-37153672

Palavras-chave

Pompe disease (glycogen storage disease type II); artificial intelligence (AI); electronic health records (EHR); orphan disease; rare disease (RD); retrospective screening

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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Neurol Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Áustria

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