Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning

Introduction Developing cross‐validated multi‐biomarker models for the prediction of the rate of cognitive decline in Alzheimer's disease (AD) is a critical yet unmet clinical challenge. Methods We applied support vector regression to AD biomarkers derived from cerebrospinal fluid, structural m...

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Veröffentlicht in:	Alzheimer's & dementia 2020-03, Vol.16 (3), p.501-511
Hauptverfasser:	Franzmeier, Nicolai, Koutsouleris, Nikolaos, Benzinger, Tammie, Goate, Alison, Karch, Celeste M., Fagan, Anne M., McDade, Eric, Duering, Marco, Dichgans, Martin, Levin, Johannes, Gordon, Brian A., Lim, Yen Ying, Masters, Colin L., Rossor, Martin, Fox, Nick C., O'Connor, Antoinette, Chhatwal, Jasmeer, Salloway, Stephen, Danek, Adrian, Hassenstab, Jason, Schofield, Peter R., Morris, John C., Bateman, Randall J., Ewers, Michael
Format:	Artikel
Sprache:	eng
Schlagworte:	Adult Alzheimer Disease - genetics Alzheimer Disease - pathology Alzheimer's disease autosomal‐dominant Alzheimer's disease biomarkers Biomarkers - cerebrospinal fluid Cognitive Dysfunction - genetics Cognitive Dysfunction - pathology Disease Progression Female Humans Machine Learning Magnetic Resonance Imaging Male MRI PET Positron-Emission Tomography progression prediction risk enrichment
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