Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression

Hongtao Hao, Joseph L. Austerweil,  the Alzheimer’s Disease Neuroimaging Initiative
Proceedings of the Fifth Machine Learning for Health Symposium, PMLR 297:1467-1515, 2026.

Abstract

Event-based models ({EBM}s) infer disease progression from cross-sectional data, and standard {EBM}s assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model ({JPM}), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several {JPM} variants (Pairwise, Bradley–Terry, Plackett–Luce, and Mallows) and analyze three properties: (i) calibration–whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation–the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness–the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, {JPM} improves ordering accuracy by roughly 21% over a strong {EBM} baseline ({SA}-{EBM}) that treats the joint disease as a single condition. Finally, using {NACC}, we find that the Mallows variant of {JPM} and the baseline model ({SA}-{EBM}) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of {AD} and {VaD}.

Cite this Paper

BibTeX
@InProceedings{pmlr-v297-hao26b, title = {Joint Progression Modeling ({JPM}): A Probabilistic Framework for Mixed-Pathology Progression}, author = {Hao, Hongtao and Austerweil, Joseph L. and {the Alzheimer's Disease Neuroimaging Initiative}}, booktitle = {Proceedings of the Fifth Machine Learning for Health Symposium}, pages = {1467--1515}, year = {2026}, editor = {Argaw, Peniel and Zhang, Haoran and Jabbour, Sarah and Chandak, Payal and Ji, Jerry and Mukherjee, Sumit and Salaudeen, Olawale and Chang, Trenton and Healey, Elizabeth and Gröger, Fabian and Adibi, Amin and Hegselmann, Stefan and Wild, Benjamin and Noori, Ayush}, volume = {297}, series = {Proceedings of Machine Learning Research}, month = {13--14 Dec}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v297/main/assets/hao26b/hao26b.pdf}, url = {https://proceedings.mlr.press/v297/hao26b.html}, abstract = {Event-based models ({EBM}s) infer disease progression from cross-sectional data, and standard {EBM}s assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model ({JPM}), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several {JPM} variants (Pairwise, Bradley–Terry, Plackett–Luce, and Mallows) and analyze three properties: (i) calibration–whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation–the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness–the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, {JPM} improves ordering accuracy by roughly 21% over a strong {EBM} baseline ({SA}-{EBM}) that treats the joint disease as a single condition. Finally, using {NACC}, we find that the Mallows variant of {JPM} and the baseline model ({SA}-{EBM}) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of {AD} and {VaD}.} }
Endnote
%0 Conference Paper %T Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression %A Hongtao Hao %A Joseph L. Austerweil %A the Alzheimer’s Disease Neuroimaging Initiative %B Proceedings of the Fifth Machine Learning for Health Symposium %C Proceedings of Machine Learning Research %D 2026 %E Peniel Argaw %E Haoran Zhang %E Sarah Jabbour %E Payal Chandak %E Jerry Ji %E Sumit Mukherjee %E Olawale Salaudeen %E Trenton Chang %E Elizabeth Healey %E Fabian Gröger %E Amin Adibi %E Stefan Hegselmann %E Benjamin Wild %E Ayush Noori %F pmlr-v297-hao26b %I PMLR %P 1467--1515 %U https://proceedings.mlr.press/v297/hao26b.html %V 297 %X Event-based models ({EBM}s) infer disease progression from cross-sectional data, and standard {EBM}s assume a single underlying disease per individual. In contrast, mixed pathologies are common in neurodegeneration. We introduce the Joint Progression Model ({JPM}), a probabilistic framework that treats single-disease trajectories as partial rankings and builds a prior over joint progressions. We study several {JPM} variants (Pairwise, Bradley–Terry, Plackett–Luce, and Mallows) and analyze three properties: (i) calibration–whether lower model energy predicts smaller distance to the ground truth ordering; (ii) separation–the degree to which sampled rankings are distinguishable from random permutations; and (iii) sharpness–the stability of sampled aggregate rankings. All variants are calibrated, and all achieve near-perfect separation; sharpness varies by variant and is well-predicted by simple features of the input partial rankings (number and length of rankings, conflict, and overlap). In synthetic experiments, {JPM} improves ordering accuracy by roughly 21% over a strong {EBM} baseline ({SA}-{EBM}) that treats the joint disease as a single condition. Finally, using {NACC}, we find that the Mallows variant of {JPM} and the baseline model ({SA}-{EBM}) have results that are more consistent with prior literature on the possible disease progression of the mixed pathology of {AD} and {VaD}.
APA
Hao, H., Austerweil, J.L. & the Alzheimer’s Disease Neuroimaging Initiative, . (2026). Joint Progression Modeling (JPM): A Probabilistic Framework for Mixed-Pathology Progression. Proceedings of the Fifth Machine Learning for Health Symposium, in Proceedings of Machine Learning Research 297:1467-1515 Available from https://proceedings.mlr.press/v297/hao26b.html.

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