Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer’s Disease

Vasiliki Tassopoulou, Fanyang Yu, Christos Davatzikos
Proceedings of the 2nd Machine Learning for Health symposium, PMLR 193:539-551, 2022.

Abstract

Longitudinal prediction of Alzheimer’s disease progression is of high importance for early diagnosis and clinical trial design. We propose to predict the longitudinal changes of neuroimaging biomarkers and cognitive scores by leveraging the expressivity of Deep Kernel Learning with single-task Gaussian Processes. The temporal function that describes the progression of the biomarker is learned through a Gaussian Process. By learning these temporal functions we can predict any future value of a clinical variable. We apply our method for extrapolation of neuroimaging biomarkers, SPARE-AD index, and cognitive metric Adas-Cog13, both significant predictors for the pathological and cognitive changes of Alzheimer’s Disease. The method has been validated in two cohorts, ADNI and BLSA, where the results show that the proposed method significantly outperforms baselines and state-of-the-art models in AD progression prediction both on providing point estimates and quantifying uncertainty.

Cite this Paper

BibTeX
@InProceedings{pmlr-v193-tassopoulou22a, title = {Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer’s Disease}, author = {Tassopoulou, Vasiliki and Yu, Fanyang and Davatzikos, Christos}, booktitle = {Proceedings of the 2nd Machine Learning for Health symposium}, pages = {539--551}, year = {2022}, editor = {Parziale, Antonio and Agrawal, Monica and Joshi, Shalmali and Chen, Irene Y. and Tang, Shengpu and Oala, Luis and Subbaswamy, Adarsh}, volume = {193}, series = {Proceedings of Machine Learning Research}, month = {28 Nov}, publisher = {PMLR}, pdf = {https://proceedings.mlr.press/v193/tassopoulou22a/tassopoulou22a.pdf}, url = {https://proceedings.mlr.press/v193/tassopoulou22a.html}, abstract = {Longitudinal prediction of Alzheimer’s disease progression is of high importance for early diagnosis and clinical trial design. We propose to predict the longitudinal changes of neuroimaging biomarkers and cognitive scores by leveraging the expressivity of Deep Kernel Learning with single-task Gaussian Processes. The temporal function that describes the progression of the biomarker is learned through a Gaussian Process. By learning these temporal functions we can predict any future value of a clinical variable. We apply our method for extrapolation of neuroimaging biomarkers, SPARE-AD index, and cognitive metric Adas-Cog13, both significant predictors for the pathological and cognitive changes of Alzheimer’s Disease. The method has been validated in two cohorts, ADNI and BLSA, where the results show that the proposed method significantly outperforms baselines and state-of-the-art models in AD progression prediction both on providing point estimates and quantifying uncertainty.} }
Endnote
%0 Conference Paper %T Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer’s Disease %A Vasiliki Tassopoulou %A Fanyang Yu %A Christos Davatzikos %B Proceedings of the 2nd Machine Learning for Health symposium %C Proceedings of Machine Learning Research %D 2022 %E Antonio Parziale %E Monica Agrawal %E Shalmali Joshi %E Irene Y. Chen %E Shengpu Tang %E Luis Oala %E Adarsh Subbaswamy %F pmlr-v193-tassopoulou22a %I PMLR %P 539--551 %U https://proceedings.mlr.press/v193/tassopoulou22a.html %V 193 %X Longitudinal prediction of Alzheimer’s disease progression is of high importance for early diagnosis and clinical trial design. We propose to predict the longitudinal changes of neuroimaging biomarkers and cognitive scores by leveraging the expressivity of Deep Kernel Learning with single-task Gaussian Processes. The temporal function that describes the progression of the biomarker is learned through a Gaussian Process. By learning these temporal functions we can predict any future value of a clinical variable. We apply our method for extrapolation of neuroimaging biomarkers, SPARE-AD index, and cognitive metric Adas-Cog13, both significant predictors for the pathological and cognitive changes of Alzheimer’s Disease. The method has been validated in two cohorts, ADNI and BLSA, where the results show that the proposed method significantly outperforms baselines and state-of-the-art models in AD progression prediction both on providing point estimates and quantifying uncertainty.
APA
Tassopoulou, V., Yu, F. & Davatzikos, C.. (2022). Deep Kernel Learning with Temporal Gaussian Processes for Clinical Variable Prediction in Alzheimer’s Disease. Proceedings of the 2nd Machine Learning for Health symposium, in Proceedings of Machine Learning Research 193:539-551 Available from https://proceedings.mlr.press/v193/tassopoulou22a.html.

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