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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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| Publicado no: | Brain Behav |
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| Main Authors: | , , , , , , |
| Formato: | Artigo |
| Idioma: | Inglês |
| Publicado em: |
John Wiley and Sons Inc.
2017
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| Assuntos: | |
| Acesso em linha: | https://ncbi.nlm.nih.gov/pmc/articles/PMC5516607/ https://ncbi.nlm.nih.gov/pubmed/28729939 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.1002/brb3.733 |
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