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

Abstract

Abstract 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.

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