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Predicting sporadic Alzheimer's disease progression via inherited Alzheimer's disease‐informed machine‐learning
Alzheimer's & Dementia ( IF 13.0 ) Pub Date : 2020-02-11 , DOI: 10.1002/alz.12032
Nicolai Franzmeier 1 , Nikolaos Koutsouleris 2 , Tammie Benzinger 3, 4 , Alison Goate 5, 6 , Celeste M Karch 4, 7, 8 , Anne M Fagan 4, 7, 9 , Eric McDade 4, 9 , Marco Duering 1 , Martin Dichgans 1, 10, 11 , Johannes Levin 10, 11, 12 , Brian A Gordon 4, 13, 14 , Yen Ying Lim 15 , Colin L Masters 15 , Martin Rossor 16 , Nick C Fox 16 , Antoinette O'Connor 16 , Jasmeer Chhatwal 17 , Stephen Salloway 18 , Adrian Danek 12 , Jason Hassenstab 4, 9, 14 , Peter R Schofield 19, 20 , John C Morris 4, 8, 9 , Randall J Bateman 4, 9 , 21 , 22 , Michael Ewers 1
Affiliation  

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.

中文翻译:

通过遗传性阿尔茨海默病知情机器学习预测散发性阿尔茨海默病进展

开发交叉验证的多生物标志物模型来预测阿尔茨海默病 (AD) 认知能力下降的速度是一项关键但尚未解决的临床挑战。
更新日期:2020-02-11
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