Machine learning for comprehensive forecasting of Alzheimer’s Disease progression

Most approaches to machine learning from electronic health data can only predict a single endpoint. The ability to simultaneously simulate dozens of patient characteristics is a crucial step towards personalized medicine for Alzheimer’s Disease. Here, we use an unsupervised machine learning model ca...

Descripción completa

Detalles Bibliográficos
Autores principales: Fisher, Charles K., Smith, Aaron M., Walsh, Jonathan R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754403/
https://www.ncbi.nlm.nih.gov/pubmed/31541187
http://dx.doi.org/10.1038/s41598-019-49656-2

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6754403/
https://www.ncbi.nlm.nih.gov/pubmed/31541187
http://dx.doi.org/10.1038/s41598-019-49656-2

Ejemplares similares