Unsupervised Machine Learning to Identify Separable Clinical Alzheimer’s Disease Sub-Populations

Heterogeneity among Alzheimer’s disease (AD) patients confounds clinical trial patient selection and therapeutic efficacy evaluation. This work defines separable AD clinical sub-populations using unsupervised machine learning. Clustering (t-SNE followed by k-means) of patient features and associatio...

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Detalles Bibliográficos
Autores principales: Prakash, Jayant, Wang, Velda, Quinn, Robert E., Mitchell, Cassie S.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392842/
https://www.ncbi.nlm.nih.gov/pubmed/34439596
http://dx.doi.org/10.3390/brainsci11080977

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8392842/
https://www.ncbi.nlm.nih.gov/pubmed/34439596
http://dx.doi.org/10.3390/brainsci11080977

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