A causal learning framework for the analysis and interpretation of COVID-19 clinical data

We present a workflow for clinical data analysis that relies on Bayesian Structure Learning (BSL), an unsupervised learning approach, robust to noise and biases, that allows to incorporate prior medical knowledge into the learning process and that provides explainable results in the form of a graph...

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Detalles Bibliográficos
Autores principales: Ferrari, Elisa, Gargani, Luna, Barbieri, Greta, Ghiadoni, Lorenzo, Faita, Francesco, Bacciu, Davide
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/PMC9119448/
https://www.ncbi.nlm.nih.gov/pubmed/35588440
http://dx.doi.org/10.1371/journal.pone.0268327

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9119448/
https://www.ncbi.nlm.nih.gov/pubmed/35588440
http://dx.doi.org/10.1371/journal.pone.0268327

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