Multimodal ensemble model for Alzheimer's disease conversion prediction from Early Mild Cognitive Impairment subjects

Alzheimer's Disease (AD) is the most common type of dementia. Predicting the conversion to Alzheimer's from the mild cognitive impairment (MCI) stage is a complex problem that has been studied extensively. This study centers on individualized EMCI (the earliest MCI subset) to AD conversion...

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Veröffentlicht in:	Computers in biology and medicine Jg. 151; H. Pt A; S. 106201
Hauptverfasser:	Velazquez, Matthew, Lee, Yugyung
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Elsevier Ltd 01.12.2022 Elsevier Limited
Schlagworte:	Accuracy Algorithms Alzheimer Disease - diagnostic imaging Alzheimer's disease Artificial neural networks Biomarkers Classification Classifiers Cognitive ability Cognitive Dysfunction - diagnostic imaging Conversion Convolutional neural network Deep learning Dementia disorders Diffusion Diffusion Tensor Imaging Electronic health records Electronic medical records Ensemble model with grid search Explainability Feature selection Humans Impairment Internal Medicine Longitudinal studies Machine learning Magnetic resonance imaging Magnetic Resonance Imaging - methods Mathematical analysis Medical imaging Mild cognitive impairment Mortality Neural networks Neurodegenerative diseases Neuroimaging Neuroimaging - methods Neuropsychology Other Patients Predictions Random forest Search algorithms Support vector machines Tensors Mild cognitive impairment Convolutional neural network Machine learning Random forest Longitudinal studies Electronic health records Explainability Alzheimer's disease Ensemble model with grid search Diffusion tensor imaging
ISSN:	0010-4825, 1879-0534, 1879-0534
Online-Zugang:	Volltext
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