English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The ability to predict the progression of biomarkers, notably in NDD, islimited by the size of the longitudinal data sets, in terms of number ofpatients, number of visits per patients and total follow-up time. To this end,we introduce a data augmentation technique that is able to reproduce thevariability seen in a longitudinal training data set and simulate continuousbiomarkers trajectories for any number of virtual patients. Thanks to thissimulation framework, we propose to transform the training set into a simulateddata set with more patients, more time-points per patient and longer follow-upduration. We illustrate this approach on the prediction of the MMSE of MCIsubjects of the ADNI data set. We show that it allows to reach predictions witherrors comparable to the noise in the data, estimated in test/retest studies,achieving a improvement of 37% of the mean absolute error compared to the samenon-augmented model.

Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects