Large-language-model-based 10-year risk prediction of cardiovascular disease: insight from the UK biobank data

Abstract

Abstract Background Conventional cardiovascular risk prediction models provide insights into population-level risk factors and have been widely adopted in clinical practice. However, these models have limited generalizability and flexibility. Large language models (LLMs) have demonstrated remarkable proficiency for use in various industries. Methods In this study, we have investigated the feasibility of Large Language Models (LLMs) such as ChatGPT-3.5, ChatGPT-4, and Bard for predicting 10-year cardiovascular risk of a patient. We used data from the UK Biobank Cohort, a major biomedical database in the UK, and the Korean Genome and Epidemiology Study (KoGES), a large-scale prospective study in Korea, for additional validation and multi-institutional research. These databases provided a wide array of information including age, sex, medical history, lipid profile, blood pressure, and physical measurement. Based on these data, we generated language sentences for individual analysis and input these into the LLM to derive results. The performance of the LLMs was then compared with the Framingham Risk Score (FRS), a conventional risk prediction model, using this real-world data. We confirmed the model performance of both the LLMs and FRS, evaluating their accuracy, sensitivity, specificity, Positive Predictive Value (PPV), Negative Predictive Value (NPV), and F1 score. Their performance in predicting 10-year cardiovascular risk was compared through Kaplan-Meier survival analysis and Cox-hazard ratio analysis. Findings GPT-4 achieved performance comparable to the FRS in cardiovascular risk prediction in both the UK Biobank {accuracy (0·834 vs· 0·773) and F1 score (0·138 vs· 0·132)} and KoGES {accuracy (0·902 vs· 0·874)}. The Kaplan–Meier survival analysis of GPT-4 demonstrated distinct survival patterns among groups, which revealed a strong association between the GPT risk prediction output and survival outcomes. The additional analysis of limited variables using GPT-3·5 indicated that ChatGPT’s prediction performance was preserved despite the omission of a few variables in the prompt, especially without physical measurement data Interpretation This study proposed that ChatGPT can achieve performance comparable to conventional models in predicting cardiovascular risk. Furthermore, ChatGPT exhibits enhanced accessibility, flexibility, and the ability to provide user-friendly outputs. With the evolution of LLMs, such as ChatGPT, studies should focus on applying LLMs to various medical scenarios and subsequently optimizing their performance.

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