1 code implementation • 1 Dec 2017 • Kelly Peterson, Ognjen Rudovic, Ricardo Guerrero, Rosalind W. Picard
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE, ADAS-Cog13, CDRSB and CS) based on each patient's previous visits.
1 code implementation • 22 Feb 2018 • Yuria Utsumi, Ognjen Rudovic, Kelly Peterson, Ricardo Guerrero, Rosalind W. Picard
In this paper, we introduce the use of a personalized Gaussian Process model (pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- using data from each patient's previous visits, and testing on future (held-out) data.
no code implementations • 19 Apr 2019 • Ognjen Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard
We introduce a novel personalized Gaussian Process Experts (pGPE) model for predicting per-subject ADAS-Cog13 cognitive scores -- a significant predictor of Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12, 18, and 24 months.
1 code implementation • ACL 2020 • Alec Chapman, Kelly Peterson, Augie Turano, Tam{\'a}ra Box, Katherine Wallace, Makoto Jones
Timely and accurate accounting of positive cases has been an important part of the response to the COVID-19 pandemic.