Jianfeng Feng

Medical language models handle knowledge questions but struggle to infer disease trajectories from an individual's repeated measurements, because literature and exams give little supervision on how indicators covary over time. HealthFound converts records from 501,936 UK Biobank participants into readable longitudinal health profiles and mixes them with medical and general reasoning data, training in three stages: curriculum supervised fine-tuning, rejection-sampled retention fine-tuning and verifiable policy optimization. Across 1,162 endpoints it reaches about 69% mean accuracy, 10.0 percentage points above the strongest comparator within each clinical chapter on average, and it also leads in zero-shot transfer to MIMIC-IV and NHANES.

Medical language models handle knowledge questions but struggle to infer disease trajectories from an individual's repeated measurements, because literature and exams give little supervision on how indicators covary over time. HealthFound converts records from 501,936 UK Biobank participants into readable longitudinal health profiles and mixes them with medical and general reasoning data, training in three stages: curriculum supervised fine-tuning, rejection-sampled retention fine-tuning and verifiable policy optimization. Across 1,162 endpoints it reaches about 69% mean accuracy, 10.0 percentage points above the strongest comparator within each clinical chapter on average, and it also leads in zero-shot transfer to MIMIC-IV and NHANES.