A machine learning framework supporting prospective clinical decisions applied to risk prediction in oncology

We present a general framework for developing a machine learning (ML) tool that supports clinician assessment of patient risk using electronic health record-derived real-world data and apply the framework to a quality improvement use case in an oncology setting to identify patients at risk for a nea...

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Detalles Bibliográficos
Autores principales: Coombs, Lorinda, Orlando, Abigail, Wang, Xiaoliang, Shaw, Pooja, Rich, Alexander S., Lakhtakia, Shreyas, Titchener, Karen, Adamson, Blythe, Miksad, Rebecca A., Mooney, Kathi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2022
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380664/
https://www.ncbi.nlm.nih.gov/pubmed/35974092
http://dx.doi.org/10.1038/s41746-022-00660-3

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9380664/
https://www.ncbi.nlm.nih.gov/pubmed/35974092
http://dx.doi.org/10.1038/s41746-022-00660-3

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