Use of Machine Learning to Identify Follow-Up Recommendations in Radiology Reports.

Carrodeguas, Emmanuel; Lacson, Ronilda; Swanson, Whitney; Khorasani, Ramin

Carrodeguas, Emmanuel; Lacson, Ronilda; Swanson, Whitney; Khorasani, Ramin.

Afiliación

Carrodeguas E; Harvard Medical School, Boston, Massachusetts; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts. Electronic address: emmanuel_carrodeguas@hms.harvard.edu.
Lacson R; Harvard Medical School, Boston, Massachusetts; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts.
Swanson W; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts.
Khorasani R; Harvard Medical School, Boston, Massachusetts; Center for Evidence-Based Imaging, Department of Radiology, Brigham and Women's Hospital, Brookline, Massachusetts.

J Am Coll Radiol ; 16(3): 336-343, 2019 Mar.

Article en En | MEDLINE | ID: mdl-30600162

Asunto(s)

Continuidad de la Atención al Paciente; Diagnóstico por Imagen; Aprendizaje Automático; Benchmarking; Adhesión a Directriz; Humanos; Procesamiento de Lenguaje Natural

Palabras clave

Machine learning; deep learning; follow-up recommendations; natural language processing; radiology report

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Diagnóstico por Imagen / Continuidad de la Atención al Paciente / Aprendizaje Automático Tipo de estudio: Diagnostic_studies / Prognostic_studies Límite: Humans Idioma: En Revista: J Am Coll Radiol Asunto de la revista: RADIOLOGIA Año: 2019 Tipo del documento: Article Pais de publicación: Estados Unidos

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