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Sep 14, 2018 · We present a computational framework comprising machine-learning techniques for 1) modeling symptom trajectories and 2) prediction of symptom trajectories ...
Sep 14, 2018 · The presented longitudinal framework comprises two tasks, 1) trajectory modeling and 2) trajectory prediction. The overall process is outlined ...
Analysis workflow of the longitudinal framework. The workflow comprises two tasks, 1) trajectory modeling (TM), and 2) trajectory prediction (TP). Data from ...
Abstract: Computational models predicting symptomatic progression at the individual level can be highly beneficial for early intervention and treatment ...
We model the prototypical symptom trajectory classes using clinical assessment scores from mini-mental state exam (MMSE) and Alzheimer's Disease Assessment ...
Jun 12, 2019 · This machine-learning model is capable of combining longitudinal, multimodal data (MRI, cognitive, genetic and demographic data) to predict the ...
Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data ; Journal: PLOS Computational Biology, 2018, № 9, p.
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May 10, 2024 · Thus, our multimodal trajectory modeling approach provides a cost-effective and non-invasive tool for early dementia prediction without labeled ...
Sep 14, 2018 · Modeling and prediction of clinical symptom trajectories in Alzheimer's disease using longitudinal data. PLoS Comput Biol. 2018 Sep;14(9): ...
Our model identified 10 disease-related states in Alzheimer's disease trajectory. The identified states constituted 3 unique stages and 2 progression patterns.
Missing: symptom | Show results with:symptom