Auto-MedCalc: Automated Biomarkers Discovery and Risk Score Generation with AI Agents

Identifying biomarkers and generating risk scores are usually essential tasks in many biomedicine and clinical scenarios. However, this is a highly hypothesis-driven and experience-dependent process requiring extensive experiments as well. How to discover biomarkers from multi-modality, multi-source data and generate risk scores with higher precision motivates us to design the framework Auto-MedCalc, a data-driven pipeline to automatically identify biomarkers and generate risk scores for diagnosis. Auto-MedCalc is a multi-agent AI system with large language models and computational tools. Three representative studies of transplantation from the public ImmPort data portal are used for the experiment. Auto-MedCalc achieved 0.93, 0.88, and 0.88 ROC-AUC on the rejection prediction after kidney, liver, and heart transplants, surpassing the best expert-designed medical calculators by 50%, 4.7%, and 39%, respectively. Auto-MedCalc also validated the previously found human biomarkers and discovered new biomarkers, β-Glucuronidase and Alpha-1-microglobulin, in heart transplants, which hadn’t attracted much attention before. The superior performance from experiments shows that Auto-MedCalc can be used for both clinical applications and scientific discovery.