HealthFormer: A Generative Multimodal Model for Simulating Human Physiology and Clinical Interventions

HealthFormer is a decoder-only Transformer-based generative multimodal model trained on deep phenotype data from over 15,000 individuals. It can model human physiological trajectories, outperform traditional clinical risk scores in 27 out of 30 disease and mortality endpoints, and accurately simulate the effects of personalized nutritional interventions, laying the foundation for personalized medicine and clinical digital twin technologies.

Traditional medical prediction relies on statistical models and clinical scoring systems, which only consider a few risk factors, fail to capture the complex interactions of physiological systems, and struggle to predict individual responses to interventions. With the popularization of technologies like wearable devices, CGM (Continuous Glucose Monitoring), and microbiome sequencing, massive heterogeneous health data has emerged, but integration and insight extraction remain challenging—thus HealthFormer was developed.

HealthFormer adopts a decoder-only Transformer architecture. Its training data comes from over 15,000 participants in the Human Phenotype Project, integrating 7 types of indicators: blood biomarkers, body composition, sleep physiology, continuous glucose monitoring, gut microbiome, wearable data, behavior, and drug exposure. Generative modeling treats physiological trajectories as a sequence generation problem, which can naturally handle missing data, quantify uncertainty, and support multiple downstream tasks (risk assessment, disease prediction, intervention simulation) without separate training.

Experimental validation shows that HealthFormer, without fine-tuning, transfers to 4 external cohorts and outperforms traditional scores in 27 out of 30 endpoints; in personalized nutrition trials, predicted changes in biomarkers are highly correlated with measured ones (e.g., Pearson correlation coefficient of 0.78 for diastolic blood pressure changes); in 41 RCTs, the direction of effect is predicted correctly 100% of the time, and 73% of predicted means fall within the reported 95% confidence interval.

HealthFormer is positioned as an initial health world model, drawing on the concept of autonomous driving world models, and can uniformly handle prediction, risk stratification, and intervention simulation. It lays the foundation for clinical digital twins, enabling functions such as virtual clinical trials, personalized treatment optimization, and early warning systems, and promoting the transformation of healthcare from population-based to individual-based and from static to dynamic.

Currently, HealthFormer has issues such as limited population in training data, reliance on statistical correlations rather than causal mechanisms, and privacy and security concerns. In the future, it is necessary to further verify its generalization in different ethnic/age/disease populations, interpret prediction results with medical knowledge, and develop and deploy it strictly in accordance with data protection frameworks.