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

• Published in: Computers in biology and medicine Vol. 151; no. Pt A; p. 106201
• Main Authors: Velazquez, Matthew, Lee, Yugyung
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.12.2022; Elsevier Limited
• Subjects: 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

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...