Feature selective temporal prediction of Alzheimer's disease progression using hippocampus surface morphometry

INTRODUCTION: Prediction of Alzheimer's disease (AD) progression based on baseline measures allows us to understand disease progression and has implications in decisions concerning treatment strategy. To this end, we combine a predictive multi‐task machine learning method (cFSGL) with a novel M...

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Detalles Bibliográficos
Autores principales: Tsao, Sinchai, Gajawelli, Niharika, Zhou, Jiayu, Shi, Jie, Ye, Jieping, Wang, Yalin, Leporé, Natasha
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2017
Materias:
Original Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516607/
https://www.ncbi.nlm.nih.gov/pubmed/28729939
http://dx.doi.org/10.1002/brb3.733

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5516607/
https://www.ncbi.nlm.nih.gov/pubmed/28729939
http://dx.doi.org/10.1002/brb3.733

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