arXiv:2606. 24604v1 Announce Type: new Abstract: Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is.
By Arya Hariharan, Shreyank N Gowda, Anala M R
Longitudinal modelling of Alzheimer's disease progression is clinically useful only if it can describe not just the most likely next diagnosis, but how a patient may evolve over time and how reliable that forecast is. Most deep learning approaches reduce this problem to single-step classification, treating cognitively normal, mild cognitive impairment, and dementia as flat categories while providing limited insight into how uncertainty accumulates across future visits.
The paper introduces a reverse spatio‑temporal disease progression model that reconstructs unobserved healthier anatomy from later diseased scans. It employs a two‑stage architecture: a frozen 3D vector‑quantised autoencoder creates a discrete latent space, and a Neural ODE learns continuous‑time dynamics, with a recurrent encoder initializing the latent state from reverse‑ordered observations. Experiments on a synthetic Morpho‑MNIST benchmark and longitudinal Alzheimer’s MRIs show the model can recover unseen prior states and outperform baseline methods in predicting healthy trajectories.
By Ulugbek Shernazarov, Moucheng Xu, Inomjon Ramatov
arXiv:2606. 30398v1 Announce Type: new Abstract: Accurately predicting the temporal evolution of clinical biomarkers is crucial for the early diagnosis and management of neurodegenerative diseases such as Alzheimer's disease.
By Yujee Song, Seunghun Baek, Guorong Wu, Won Hwa Kim
NOAH is a generative transformer that models the entire multimodal patient journey by integrating bidirectional time and a variational latent space to capture continuous, stochastic clinical trajectories. Trained on over 559 million events from 431,000 hospital visits, it processes medical images, time‑series, numeric signals, categorical events, and both structured and unstructured records. The model supports autoregressive forecasting, zero‑shot classification, and counterfactual simulations, yielding strong predictive performance across 15 ICD chapters, 29 comorbidities, and time‑to‑event outcomes.
NOAH is a generative transformer that learns the full multimodal patient journey by integrating bidirectional time and a variational latent space. Trained on over 559 million clinical events from 431,000 hospital visits, it processes medical images, time‑series, numeric signals, categorical events, and both structured and unstructured records. The model supports autoregressive forecasting, zero‑shot classification, and counterfactual intervention simulation, yielding strong performance on clinical outcomes, ICD chapters, comorbidities, and time‑to‑event prediction.
By Tobias Susetzky, Raphael Rehms, Dmitrii Seletkov, \"Ozg\"un Turgut, Michelle Espranita Liman, Lisa Steinhelfer, Rickmer Braren, Daniel Rueckert