10 citations · 11 across the 4 of their papers we have counts for
4 papers
leaspy: LEArning Spatiotemporal Patterns in PYthon
Juliette Ortholand, Sofia Kaisaridi, Nicolas Gensollen +14
Longitudinal data are fundamental across scientific disciplines for modeling how complex systems evolve over time. A core challenge in these settings is handling temporal misalignm…
The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up
Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young +93
We present the findings of "The Alzheimer's Disease Prediction Of Longitudinal Evolution" (TADPOLE) Challenge, which compared the performance of 92 algorithms from 33 international…
Simulation of virtual cohorts increases predictive accuracy of cognitive decline in MCI subjects
Igor Koval, Stéphanie Allassonnière, Stanley Durrleman
The ability to predict the progression of biomarkers, notably in NDD, is limited by the size of the longitudinal data sets, in terms of number of patients, number of visits per pat…
Statistical learning of spatiotemporal patterns from longitudinal manifold-valued networks
Igor Koval, Jean-Baptiste Schiratti, Alexandre Routier +4
We introduce a mixed-effects model to learn spatiotempo-ral patterns on a network by considering longitudinal measures distributed on a fixed graph. The data come from repeated obs…