NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Summary
NOAH introduces a generative transformer model for comprehensive representation and forecasting of longitudinal multimodal patient data, enabling tasks like zero-shot classification and counterfactual simulation in clinical settings.
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Paper page - NOAH: Learning the Full Patient Journey. A Longitudinal Multimodal Time-Aware Model for Representation and Forecasting
Source: https://huggingface.co/papers/2609.09140
Abstract
NOAH is a generative transformer that models full multimodal patient journeys with continuous time dynamics and stochastic latent states, enabling forecasting, zero-shot classification, and counterfactual simulation across diverse clinical data.
The 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. Existing AI approaches for modeling longitudinal patient records are predominantly discriminative, limited to a few modalities, constrained by closed categorical vocabularies, treating time as a monotonic inductive bias, or they are limited in forecasting future patient states. We introduce NOAH, a time-aware, task-agnostic,generative transformermodel representing and forecasting the full multimodal patient journey. NOAH features a novelbidirectional time integrationand avariational latent spaceto capture the continuous evolution of patient states and the stochasticity of clinical trajectories. Built from over 559 million clinical events from 431,000 hospital visits of 299,000 patients across the MIMIC dataset family, NOAH natively processes medical images, time-series and numeric signals, categorical events, as well as structured and unstructured clinical records. NOAH is the first truly holistic generative model in its field, enablingautoregressive forecastingwith optionaltime control,zero-shot classification, andcounterfactual interventionsimulation. It generates highly informative and predictive patient state representations that demonstrate strong performance in probing for clinical outcomes, 15 ICD chapters, and 29 comorbidities, as well as intime-to-event prediction. Seamlessly handling diverse modalities and complex temporal dynamics, NOAH provides a versatile, task-agnostic, scalable foundation for intelligent predictive systems in personalized clinical care and digital medicine.
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