2019
DOI: 10.1016/j.neurobiolaging.2019.08.032
|View full text |Cite
|
Sign up to set email alerts
|

Biomarker-guided clustering of Alzheimer's disease clinical syndromes

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
3
2

Citation Types

0
26
0

Year Published

2020
2020
2023
2023

Publication Types

Select...
5
1

Relationship

0
6

Authors

Journals

citations
Cited by 44 publications
(27 citation statements)
references
References 57 publications
0
26
0
Order By: Relevance
“…Traditional Caroli and Frisoni, 2010;Jack et al, 2011Jack et al, , 2012 Machine learning Generative Fonteijn et al, 2012;Chen et al, 2016;Khanna et al, 2018;Oxtoby et al, 2018;Basu et al, 2019;De Jong et al, 2019;Gootjes-Dreesbach et al, 2019;Martinez-Murcia et al, 2019 Discriminative Supervised Hinrichs et al, 2010;Magnin et al, 2010;Rao et al, 2011;Zhang et al, 2011;Da et al, 2013;Li et al, 2013Unsupervised Nettiksimmons et al, 2014Gamberger et al, 2017;Toschi et al, 2019 We subdivide data-driven integrative AD models which into two subgroups. While the first group uses simple statistical approaches (e.g., simple linear models), the second group uses more advanced techniques (e.g., machine learning).…”
Section: Data-driven Integrative Ad Models Referencesmentioning
confidence: 99%
See 4 more Smart Citations
“…Traditional Caroli and Frisoni, 2010;Jack et al, 2011Jack et al, , 2012 Machine learning Generative Fonteijn et al, 2012;Chen et al, 2016;Khanna et al, 2018;Oxtoby et al, 2018;Basu et al, 2019;De Jong et al, 2019;Gootjes-Dreesbach et al, 2019;Martinez-Murcia et al, 2019 Discriminative Supervised Hinrichs et al, 2010;Magnin et al, 2010;Rao et al, 2011;Zhang et al, 2011;Da et al, 2013;Li et al, 2013Unsupervised Nettiksimmons et al, 2014Gamberger et al, 2017;Toschi et al, 2019 We subdivide data-driven integrative AD models which into two subgroups. While the first group uses simple statistical approaches (e.g., simple linear models), the second group uses more advanced techniques (e.g., machine learning).…”
Section: Data-driven Integrative Ad Models Referencesmentioning
confidence: 99%
“…Within this subtype, models can be characterized as discriminative or generative. Discriminative models are designed to discriminate between groups (e.g., cases and controls) and can be further described as supervised or unsupervised, depending on whether they rely on labeled (Hinrichs et al, 2011;Da et al, 2013) or unlabeled (Toschi et al, 2019) data. Generative models contribute to disease understanding by automatically learning the inherent distribution of a dataset and its feature interdependencies .…”
Section: Data-driven Integrative Ad Models Referencesmentioning
confidence: 99%
See 3 more Smart Citations