Explaining predictive factors in patient pathways using autoencoders

This paper introduces an end-to-end methodology to predict a pathway-related outcome and identifying predictive factors using autoencoders. A formal description of autoencoders for explainable binary predictions is presented, along with two objective functions that allows for filtering and inverting...

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Detalles Bibliográficos
Autores principales: De Oliveira, Hugo, Martin, Prodel, Ludovic, Lamarsalle, Vincent, Augusto, Xiaolan, Xie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2022
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648714/
https://www.ncbi.nlm.nih.gov/pubmed/36355757
http://dx.doi.org/10.1371/journal.pone.0277135

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9648714/
https://www.ncbi.nlm.nih.gov/pubmed/36355757
http://dx.doi.org/10.1371/journal.pone.0277135

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