Explainable artificial intelligence (XAI) for exploring spatial variability of lung and bronchus cancer (LBC) mortality rates in the contiguous USA

Machine learning (ML) has demonstrated promise in predicting mortality; however, understanding spatial variation in risk factor contributions to mortality rate requires explainability. We applied explainable artificial intelligence (XAI) on a stack-ensemble machine learning model framework to explor...

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Detalles Bibliográficos
Autores principales: Ahmed, Zia U., Sun, Kang, Shelly, Michael, Mu, Lina
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677843/
https://www.ncbi.nlm.nih.gov/pubmed/34916529
http://dx.doi.org/10.1038/s41598-021-03198-8

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8677843/
https://www.ncbi.nlm.nih.gov/pubmed/34916529
http://dx.doi.org/10.1038/s41598-021-03198-8

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