Interpretable machine learning for high-dimensional trajectories of aging health

We have built a computational model for individual aging trajectories of health and survival, which contains physical, functional, and biological variables, and is conditioned on demographic, lifestyle, and medical background information. We combine techniques of modern machine learning with an inte...

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Detalles Bibliográficos
Autores principales: Farrell, Spencer, Mitnitski, Arnold, Rockwood, Kenneth, Rutenberg, Andrew D.
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/PMC8782527/
https://www.ncbi.nlm.nih.gov/pubmed/35007286
http://dx.doi.org/10.1371/journal.pcbi.1009746

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8782527/
https://www.ncbi.nlm.nih.gov/pubmed/35007286
http://dx.doi.org/10.1371/journal.pcbi.1009746

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