LLMs for Cardiovascular Risk Prediction from Structured Clinical Data

ArXi:2606.00031v1 Announce Type: cross Coronary artery disease (CAD) remains one of the leading causes of death globally, highlighting the need for reliable predictive systems to early diagnosis and risk assessment. While traditional machine learning models perform well on structured clinical data, large language models (LLMs) present new possibilities to interpret medical information expressed in natural language. In this work, we develop a hybrid framework that bridges structured clinical data and natural-language representations for CAD prediction.