# Disease Prediction

Glyph decomposes clinical foundation-model embeddings into sparse, nameable concepts so that features previously entangled in high-dimensional vectors can be tested like clinical variables. Across abdominal CT, radiology reports and laboratory data from 41,628 patients, the study tested 604,185 concept–outcome pairs and retained 397 high-risk concepts with 4,463 Bonferroni-significant associations. Most associations reproduced in three held-out cohorts without recalibration, and the top ten concepts reproduced more often than markers recovered from the literature.

Glyph decomposes clinical foundation-model embeddings into sparse, nameable concepts so that features previously entangled in high-dimensional vectors can be tested like clinical variables. Across abdominal CT, radiology reports and laboratory data from 41,628 patients, the study tested 604,185 concept–outcome pairs and retained 397 high-risk concepts with 4,463 Bonferroni-significant associations. Most associations reproduced in three held-out cohorts without recalibration, and the top ten concepts reproduced more often than markers recovered from the literature.

IC-GNN regroups the same atlas-defined brain regions into discrete, multiscale functional communities for each person, then uses those communities to guide edge refinement and region-specific weights in a graph neural network. Group-level atlases had smoothed over individual differences in community boundaries, while voxel-level individualized parcellations demand long scans and complicate cross-subject comparison. Across more than 30,000 participants in ABIDE, ADNI and the UK Biobank, IC-GNN reached the highest mean AUROC and accuracy among 15 baselines, with AUROC of 75.41% on ABIDE, 86.46% on ADNI and 60.62% on the UK Biobank.

IC-GNN regroups the same atlas-defined brain regions into discrete, multiscale functional communities for each person, then uses those communities to guide edge refinement and region-specific weights in a graph neural network. Group-level atlases had smoothed over individual differences in community boundaries, while voxel-level individualized parcellations demand long scans and complicate cross-subject comparison. Across more than 30,000 participants in ABIDE, ADNI and the UK Biobank, IC-GNN reached the highest mean AUROC and accuracy among 15 baselines, with AUROC of 75.41% on ABIDE, 86.46% on ADNI and 60.62% on the UK Biobank.

FERMAT, a 65-million-parameter clinical foundation model pretrained on records from 3.77 million patients, was compared against itself on the same patients: patient representations predicted five-year kidney, fatty liver, and diabetes risk better, while generated trajectories predicted the diagnoses, prescriptions, and procedures recorded together on the next clinical date better.

FERMAT, a 65-million-parameter clinical foundation model pretrained on records from 3.77 million patients, was compared against itself on the same patients: patient representations predicted five-year kidney, fatty liver, and diabetes risk better, while generated trajectories predicted the diagnoses, prescriptions, and procedures recorded together on the next clinical date better.

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.