On the importance of interpretable machine learning predictions to inform clinical decision making in oncology.

Lu, Sheng-Chieh; Swisher, Christine L; Chung, Caroline; Jaffray, David; Sidey-Gibbons, Chris

Lu, Sheng-Chieh; Swisher, Christine L; Chung, Caroline; Jaffray, David; Sidey-Gibbons, Chris.

Afiliación

Lu SC; Section of Patient-Centered Analytics, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Swisher CL; The Ronin Project, San Mateo, CA, United States.
Chung C; The Lawrence J. Ellison Institute for Transformative Medicine, Los Angeles, CA, United States.
Jaffray D; Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.
Sidey-Gibbons C; Institute for Data Science in Oncology, The University of Texas MD Anderson Cancer Center, Houston, TX, United States.

Front Oncol ; 13: 1129380, 2023.

Article en En | MEDLINE | ID: mdl-36925929

Palabras clave

decision-making support; high-stakes prediction; interpretability and explainability; opaque machine learning models; precision medicine

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies / Risk_factors_studies Idioma: En Revista: Front Oncol Año: 2023 Tipo del documento: Article País de afiliación: Estados Unidos

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