NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Researchers introduce NOAH, a generative time-aware transformer trained on 559 million MIMIC clinical events to model and forecast patient trajectories.
NOAH is a task-agnostic, time-aware generative transformer designed to represent and forecast the full multimodal patient journey across medical images, time-series signals, categorical events, and clinical text. It was trained on over 559 million clinical events from 431,000 hospital visits covering 299,000 patients in the MIMIC dataset family. The architecture combines bidirectional time integration with a variational latent space to capture continuous patient state evolution and clinical stochasticity. NOAH supports autoregressive forecasting with time control, zero-shot classification, and counterfactual intervention simulation, with evaluations on 15 ICD chapters, 29 comorbidities, and time-to-event prediction.
- Task-agnostic generative transformer for longitudinal multimodal patient records.
- Trained on 559M clinical events from 299,000 patients in the MIMIC family.
- Bidirectional time integration plus variational latent space for stochastic trajectories.
- Enables autoregressive forecasting, zero-shot classification, and counterfactual intervention simulation.
- Evaluated on 15 ICD chapters, 29 comorbidities, and time-to-event outcomes.
From the original article
1 min read at arxiv.orgThe digitization of healthcare has generated vast, longitudinal, and multimodal patient records over a lifetime, yet fully exploiting these data to represent and predict patient state trajectories remains a critical challenge. Current AI models often struggle to capture the complex, irregular temporal dynamics and inherent stochasticity of real-world multimodal patient data.
By Tobias Susetzky · arxiv.org
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