Machine learning outcome prediction using stress perfusion cardiac magnetic resonance reports and natural language processing of electronic health records.

Alskaf, Ebraham; Frey, Simon M; Scannell, Cian M; Suinesiaputra, Avan; Vilic, Dijana; Dinu, Vlad; Masci, Pier Giorgio; Perera, Divaka; Young, Alistair; Chiribiri, Amedeo

Alskaf, Ebraham; Frey, Simon M; Scannell, Cian M; Suinesiaputra, Avan; Vilic, Dijana; Dinu, Vlad; Masci, Pier Giorgio; Perera, Divaka; Young, Alistair; Chiribiri, Amedeo.

Afiliação

Alskaf E; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Frey SM; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Scannell CM; Department of Cardiology, University Hospital Basel, Basel, Switzerland.
Suinesiaputra A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Vilic D; Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, Netherlands.
Dinu V; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Masci PG; King's College London, United Kingdom.
Perera D; King's College London, United Kingdom.
Young A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.
Chiribiri A; School of Biomedical Engineering & Imaging Sciences, King's College London, United Kingdom.

Inform Med Unlocked ; 44: 101418, 2024.

Article em En | MEDLINE | ID: mdl-38173908

Palavras-chave

Cardiac magnetic resonance; Coronary artery disease; Electronic health records; Machine learning; Natural language processing; Outcome prediction

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Inform Med Unlocked Ano de publicação: 2024 Tipo de documento: Article País de afiliação: Reino Unido País de publicação: Reino Unido

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