Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes

Ognjen (Oggi) Rudovic, Yuria Utsumi, Ricardo Guerrero, Kelly Peterson, Daniel Rueckert, Rosalind W. Picard
Proceedings of the 4th Machine Learning for Healthcare Conference, PMLR 106:181-196, 2019.

Abstract

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. We start by training a population-level model using multi-modal data from previously seen subjects using a base Gaussian Process (GP) regression. Then, we personalize this model by adapting the base GP sequentially over time to a new (target) subject using domain adaptive GPs, and also by training subject-specific GP. While we show that these models achieve improved performance when selectively applied to the forecasting task (one performs better than the other on different subjects/visits), the average performance per model is suboptimal. To this end, we used the notion of meta learning in the proposed pGPE to design a regression-based weighting of these expert models, where the expert weights are optimized for each subject and his/her future visit. The results on a cohort of subjects from the ADNI dataset show that this newly introduced personalized weighting of the expert models leads to large improvements in accurately forecasting future ADAS-Cog13 scores and their fine-grained changes associated with the AD progression. This approach has potential to help identify at-risk patients early and improve the construction of clinical trials for AD.

Cite this Paper

BibTeX
@InProceedings{pmlr-v106-rudovic19a, title = {Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes}, author = {Rudovic, Ognjen (Oggi) and Utsumi, Yuria and Guerrero, Ricardo and Peterson, Kelly and Rueckert, Daniel and Picard, Rosalind W.}, booktitle = {Proceedings of the 4th Machine Learning for Healthcare Conference}, pages = {181--196}, year = {2019}, editor = {Doshi-Velez, Finale and Fackler, Jim and Jung, Ken and Kale, David and Ranganath, Rajesh and Wallace, Byron and Wiens, Jenna}, volume = {106}, series = {Proceedings of Machine Learning Research}, month = {09--10 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v106/rudovic19a/rudovic19a.pdf}, url = {https://proceedings.mlr.press/v106/rudovic19a.html}, abstract = {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. We start by training a population-level model using multi-modal data from previously seen subjects using a base Gaussian Process (GP) regression. Then, we personalize this model by adapting the base GP sequentially over time to a new (target) subject using domain adaptive GPs, and also by training subject-specific GP. While we show that these models achieve improved performance when selectively applied to the forecasting task (one performs better than the other on different subjects/visits), the average performance per model is suboptimal. To this end, we used the notion of meta learning in the proposed pGPE to design a regression-based weighting of these expert models, where the expert weights are optimized for each subject and his/her future visit. The results on a cohort of subjects from the ADNI dataset show that this newly introduced personalized weighting of the expert models leads to large improvements in accurately forecasting future ADAS-Cog13 scores and their fine-grained changes associated with the AD progression. This approach has potential to help identify at-risk patients early and improve the construction of clinical trials for AD.} }
Endnote
%0 Conference Paper %T Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes %A Ognjen (Oggi) Rudovic %A Yuria Utsumi %A Ricardo Guerrero %A Kelly Peterson %A Daniel Rueckert %A Rosalind W. Picard %B Proceedings of the 4th Machine Learning for Healthcare Conference %C Proceedings of Machine Learning Research %D 2019 %E Finale Doshi-Velez %E Jim Fackler %E Ken Jung %E David Kale %E Rajesh Ranganath %E Byron Wallace %E Jenna Wiens %F pmlr-v106-rudovic19a %I PMLR %P 181--196 %U https://proceedings.mlr.press/v106/rudovic19a.html %V 106 %X 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. We start by training a population-level model using multi-modal data from previously seen subjects using a base Gaussian Process (GP) regression. Then, we personalize this model by adapting the base GP sequentially over time to a new (target) subject using domain adaptive GPs, and also by training subject-specific GP. While we show that these models achieve improved performance when selectively applied to the forecasting task (one performs better than the other on different subjects/visits), the average performance per model is suboptimal. To this end, we used the notion of meta learning in the proposed pGPE to design a regression-based weighting of these expert models, where the expert weights are optimized for each subject and his/her future visit. The results on a cohort of subjects from the ADNI dataset show that this newly introduced personalized weighting of the expert models leads to large improvements in accurately forecasting future ADAS-Cog13 scores and their fine-grained changes associated with the AD progression. This approach has potential to help identify at-risk patients early and improve the construction of clinical trials for AD.
APA
Rudovic, O.(., Utsumi, Y., Guerrero, R., Peterson, K., Rueckert, D. & Picard, R.W.. (2019). Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes. Proceedings of the 4th Machine Learning for Healthcare Conference, in Proceedings of Machine Learning Research 106:181-196 Available from https://proceedings.mlr.press/v106/rudovic19a.html.

Related Material