Transformer Model for Alzheimer’s Disease Progression Prediction Using Longitudinal Visit Sequences

Mahdi Moghaddami, Clayton Schubring, Mohammad Siadat
Proceedings of the sixth Conference on Health, Inference, and Learning, PMLR 287:804-816, 2025.

Abstract

Alzheimer’s disease (AD) is a neurodegenerative disorder with no known cure that affects tens of millions of people worldwide. Early detection of AD is critical for timely intervention to halt or slow the progression of the disease. In this study, we propose a Transformer model for predicting the stage of AD progression at a subject’s next clinical visit using features from a sequence of visits extracted from the subject’s visit history. We also rigorously compare our model to recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent unit (GRU), and minimalRNN and assess their performances based on factors such as the length of prior visits and data imbalance. We test the importance of different feature categories and visit history, as well as compare the model to a newer Transfomer-based model optimized for time series. Our model demonstrates strong predictive performance despite missing visits and missing features in available visits, particularly in identifying converter subjects–individuals transitioning to more severe disease stages–an area that has posed significant challenges in longitudinal prediction. The results highlight the model’s potential in enhancing early diagnosis and patient outcomes.

Cite this Paper

BibTeX
@InProceedings{pmlr-v287-moghaddami25a, title = {Transformer Model for Alzheimer’s Disease Progression Prediction Using Longitudinal Visit Sequences}, author = {Moghaddami, Mahdi and Schubring, Clayton and Siadat, Mohammad}, booktitle = {Proceedings of the sixth Conference on Health, Inference, and Learning}, pages = {804--816}, year = {2025}, editor = {Xu, Xuhai Orson and Choi, Edward and Singhal, Pankhuri and Gerych, Walter and Tang, Shengpu and Agrawal, Monica and Subbaswamy, Adarsh and Sizikova, Elena and Dunn, Jessilyn and Daneshjou, Roxana and Sarker, Tasmie and McDermott, Matthew and Chen, Irene}, volume = {287}, series = {Proceedings of Machine Learning Research}, month = {25--27 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/v287/main/assets/moghaddami25a/moghaddami25a.pdf}, url = {https://proceedings.mlr.press/v287/moghaddami25a.html}, abstract = {Alzheimer’s disease (AD) is a neurodegenerative disorder with no known cure that affects tens of millions of people worldwide. Early detection of AD is critical for timely intervention to halt or slow the progression of the disease. In this study, we propose a Transformer model for predicting the stage of AD progression at a subject’s next clinical visit using features from a sequence of visits extracted from the subject’s visit history. We also rigorously compare our model to recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent unit (GRU), and minimalRNN and assess their performances based on factors such as the length of prior visits and data imbalance. We test the importance of different feature categories and visit history, as well as compare the model to a newer Transfomer-based model optimized for time series. Our model demonstrates strong predictive performance despite missing visits and missing features in available visits, particularly in identifying converter subjects–individuals transitioning to more severe disease stages–an area that has posed significant challenges in longitudinal prediction. The results highlight the model’s potential in enhancing early diagnosis and patient outcomes.} }
Endnote
%0 Conference Paper %T Transformer Model for Alzheimer’s Disease Progression Prediction Using Longitudinal Visit Sequences %A Mahdi Moghaddami %A Clayton Schubring %A Mohammad Siadat %B Proceedings of the sixth Conference on Health, Inference, and Learning %C Proceedings of Machine Learning Research %D 2025 %E Xuhai Orson Xu %E Edward Choi %E Pankhuri Singhal %E Walter Gerych %E Shengpu Tang %E Monica Agrawal %E Adarsh Subbaswamy %E Elena Sizikova %E Jessilyn Dunn %E Roxana Daneshjou %E Tasmie Sarker %E Matthew McDermott %E Irene Chen %F pmlr-v287-moghaddami25a %I PMLR %P 804--816 %U https://proceedings.mlr.press/v287/moghaddami25a.html %V 287 %X Alzheimer’s disease (AD) is a neurodegenerative disorder with no known cure that affects tens of millions of people worldwide. Early detection of AD is critical for timely intervention to halt or slow the progression of the disease. In this study, we propose a Transformer model for predicting the stage of AD progression at a subject’s next clinical visit using features from a sequence of visits extracted from the subject’s visit history. We also rigorously compare our model to recurrent neural networks (RNNs) such as long short-term memory (LSTM), gated recurrent unit (GRU), and minimalRNN and assess their performances based on factors such as the length of prior visits and data imbalance. We test the importance of different feature categories and visit history, as well as compare the model to a newer Transfomer-based model optimized for time series. Our model demonstrates strong predictive performance despite missing visits and missing features in available visits, particularly in identifying converter subjects–individuals transitioning to more severe disease stages–an area that has posed significant challenges in longitudinal prediction. The results highlight the model’s potential in enhancing early diagnosis and patient outcomes.
APA
Moghaddami, M., Schubring, C. & Siadat, M.. (2025). Transformer Model for Alzheimer’s Disease Progression Prediction Using Longitudinal Visit Sequences. Proceedings of the sixth Conference on Health, Inference, and Learning, in Proceedings of Machine Learning Research 287:804-816 Available from https://proceedings.mlr.press/v287/moghaddami25a.html.

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