Customizing Language Models with Structured Context for Cardiovascular Risk Evaluation

Abstract

This study explores an approach for customizing a large language model (LLM) for use in a medical context through fine-tuning. Structured cardiovascular knowledge and a dataset of cardiac risk cases—specifically, the Cardiovascular Disease Ontology (CVDO) and patient profiles from the Framingham dataset—were used for model adaptation. The objective was to customize a compact natural language model using different types of cardiovascular information to estimate cardiac risk. The results showed that the model’s responses varied depending on the training data, and integration analysis revealed that contextual training reshaped the model’s semantic structure. These findings highlight the model’s adaptability to different domains and the value of combining structured and semi-structured medical data sources to guide its performance.

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