Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry-Reference-Cited by-同舟云学术

Machine learning approaches to predicting amyloid status using data from an online research and recruitment registry: The Brain Health Registry

Published:2021-01 Issue:1 Volume:13 Page:
ISSN:2352-8729
Container-title:Alzheimer's & Dementia: Diagnosis, Assessment & Disease Monitoring
language:en
Short-container-title:Alz & Dem Diag Ass & Dis Mo

Author:

Albright Jack¹,Ashford Miriam T.²³,Jin Chengshi⁴,Neuhaus John⁴,Rabinovici Gil D.⁵⁶,Truran Diana²³,Maruff Paul⁷,Mackin R. Scott³⁸,Nosheny Rachel L.³⁸,Weiner Michael W.²³⁵⁶⁸

Affiliation:

1. The Nueva School San Mateo California USA

2. Department of Veterans Affairs Medical Center Northern California Institute for Research and Education (NCIRE) San Francisco California USA

3. Department of Veterans Affairs Medical Center Center for Imaging and Neurodegenerative Diseases San Francisco California USA

4. University of California San Francisco Department of Epidemiology and Biostatistics San Francisco California USA

5. Department of Radiology and Biomedical Imaging University of California San Francisco California USA

6. Department of Neurology University of California San Francisco San Francisco California USA

7. Cogstate, Ltd. Melbourne VIC Australia

8. Department of Psychiatry University of California San Francisco San Francisco California USA

Funder

National Institutes of Health

National Institute on Aging

Alzheimer's Association

California Department of Public Health

Alzheimer's Drug Discovery Foundation

Larry L. Hillblom Foundation

General Electric

Publisher

Wiley

Subject

Psychiatry and Mental health,Neurology (clinical)

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/dad2.12207

Reference49 articles.

1. Alzheimer’s disease drug-development pipeline: few candidates, frequent failures

2. Cognitive impairment and decline in cognitively normal older adults with high amyloid‐β: A meta‐analysis

3. Imaging the pathology of Alzheimer's disease: amyloid-imaging with positron emission tomography

4. PET amyloid imaging as a tool for early diagnosis and identifying patients at risk for progression to Alzheimer's disease

5. Cerebrospinal fluid analysis detects cerebral amyloid-β accumulation earlier than positron emission tomography

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