TUM releases NOAH, a longitudinal multimodal time-aware model for full patient journeys
TUM-AIMED · hf · 2026-09-10
TUM's NOAH is a generative transformer that models full multimodal patient journeys with continuous time dynamics and stochastic latent states.
- Supports forecasting, zero-shot classification, and counterfactual simulation across diverse clinical data
- Aimed at representation learning over longitudinal, time-aware medical records
- Released on Hugging Face for the medical AI research community
More from Research
- VDiff-Bench: 1,756-question benchmark shows frontier models fail at spot-the-difference — yixin_wan_ · 2026-09-10
- AutoResearchExam uses hidden test sets to study how AI agents do 24-hour research — AlexGDimakis · 2026-09-10
- Goodfire's predictive data debugging previews how LLM training will change model behavior — leland_mcinnes · 2026-09-10
- OpenAI's claimed Navier-Stokes breakthrough ignored by mainstream media — IgorCarron · 2026-09-10
- Michael Levin's new paper: a structured latent space of patterns for new forms of life and mind — danfaggella · 2026-09-10
- Do AI doomers really have a strong forecasting record? XPT study suggests otherwise — random_walker · 2026-09-10