Stanford's Ying Kejun Team Releases Pan-Modal Health World Model HealthFlux

Stanford postdoctoral researcher Ying Kejun, together with scholars from the University of Washington, Harvard Medical School, MIT, and other institutions, has released HealthFlux, the first pan-modal human health world model, on the medical preprint platform medRxiv. Trained on multimodal data from approximately 500,000 UK Biobank participants, the model uses neural ODEs and a recurrent state space model to maintain a health state described by 384 numerical values, integrating 11 data types including medical records, blood tests, nearly 3,000 plasma proteins, about 250 metabolites, and MRI. Unlike disease-specific prediction models, HealthFlux aims to infer continuous health trajectories from sparse observations, exploring technical means to significantly extend lifespan.