Search Results for author:
Found 7 papers, 3 papers with code
Computer-aided diagnosis and prediction in brain disorders
Regarding prediction, i. e. estimation of the future 'condition' of the patient, we will zoom in on use cases such as predicting the disease course in multiple sclerosis and predicting patient outcomes after treatment in brain cancer.
Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based Diagnosis and Prediction of Alzheimer's Disease
For prediction in MCI, performances decreased for both SVM (0. 665) and CNN (0. 702).
Analyzing the effect of APOE on Alzheimer's disease progression using an event-based model for stratified populations
In this work, we determined the effect of APOE alleles on the disease progression timeline of AD using a discriminative event-based model (DEBM).
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
In order to validate the biomarker ordering obtained using nDEBM, we also present a framework for Simulation of Imaging Biomarkers' Temporal Evolution (SImBioTE) that mimics neurodegeneration in brain regions.
Disease Progression Timeline Estimation for Alzheimer's Disease using Discriminative Event Based Modeling
We evaluated the proposed method on Alzheimer's Disease Neuroimaging Initiative (ADNI) data and compared the results with existing state-of-the-art EBM methods.
A Discriminative Event Based Model for Alzheimer's Disease Progression Modeling
To evaluate the accuracy, we performed extensive experiments on synthetic data simulating the progression of Alzheimer's disease.
