Application of several machine learning algorithms for the prediction of afatinib treatment outcome in advanced‐stage EGFR‐mutated non‐small‐cell lung cancer

BACKGROUND: The present study aimed to evaluate the performance of several machine learning (ML) algorithms in predicting 1‐year afatinib continuation and 2‐year survival after afatinib initiation and to identify the differences in survival outcomes between ML‐classified strata. METHODS: Data that w...

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Detalles Bibliográficos
Autores principales: Kim, Taeyun, Lee, Sang Jin, Jang, Tae‐Won
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley & Sons Australia, Ltd 2022
Materias:
Original Articles
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715822/
https://www.ncbi.nlm.nih.gov/pubmed/36278315
http://dx.doi.org/10.1111/1759-7714.14694

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9715822/
https://www.ncbi.nlm.nih.gov/pubmed/36278315
http://dx.doi.org/10.1111/1759-7714.14694

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