… of sparse regression to make more excellent prediction results. Finally, we classified …
selected subjects with s-MRI data at four longitudinal time points without conversion, baseline …

… learning-based method for the prediction of MCI-to-AD conversion within 3 years, by combining
baseline (ie… , and APOe4 genetic data from the ADNI database. We achieved a very high …

… -tuning procedure for the MCI conversion prediction task was obtained from ADNI. All subjects
… These subjects were diagnosed as MCI during the baseline visit. Among the 297 subjects, …

… sparse regression method to fuse the auxiliary data into the … data of Alzheimer’s Disease
Neuroimaging Initiative (ADNI) … to identify whether a MCI subject progresses to AD (ie, pMCI) …

… use of Jacobian Determinant (JD) from the ADNI baseline … the conversion of progressive
MCI people from stable people. … solving the problem of predicting the time-to-AD class of these …

… Specifically, using a set of features derived from the ADNI … sparse set of features with minimal
within-correlation and maximal … in predicting clinical progression from known baseline data. …

… α∈R 1× M is the target sparse coefficients. When this model is … for predicting MCI-to-AD
conversion with multimodal data. To … The ADNI data collection and sharing for this project were …

… , predictions of a transition from MCI to AD using Random Forest techniques depend strongly
on baseline … We are left with a sparse feature set, a slightly different mix of modalities than …

… are still faced with the sparsity of data on CSF and PET scans … ADNI data used in this study
were retrieved in January 2019. … , “Baseline and longitudinal patterns of brain atrophy in MCI …

… machines employing the ADNI database devoted to assessing the … 32,33 Since sparse
methods drop out the least relevant … that predicts whether the conversion holds from the MCI state …