Cardiovascular disease remains a leading cause of morbidity and mortality in the United States (US). Although high-quality data are accessible in the US for cardiovascular research, digital literacy (DL) has not been explored as a potential factor influencing cardiovascular mortality, although the Social Vulnerability Index (SVI) has been used previously as a variable in predictive modeling. Utilizing a large language model, ChatGPT4, we investigated the variability in CVD-specific mortality that could be explained by DL and SVI using regression modeling. We fitted two models to calculate the crude and adjusted CVD mortality rates. Mortality data using ICD-10 codes were retrieved from CDC WONDER, and the geographic level data was retrieved from the US Department of Agriculture. Both datasets were merged using the Federal Information Processing Standards code. The initial exploration involved data from 1999 through 2020 (n = 65,791; 99.98% complete for all US Counties) for crude cardiovascular mortality (CCM). Age-adjusted cardiovascular mortality (ACM) had data for 2020 (n = 3118 rows; 99% complete for all US Counties), with the inclusion of SVI and DL in the model (a composite of literacy and internet access). By leveraging on the advanced capabilities of ChatGPT4 and linear regression, we successfully highlighted the importance of incorporating the SVI and DL in predicting adjusted cardiovascular mortality. Our findings imply that just incorporating internet availability in the regression model may not be sufficient without incorporating significant variables, such as DL and SVI, to predict ACM. Further, our approach could enable future researchers to consider DL and SVI as key variables to study other health outcomes of public-health importance, which could inform future clinical practices and policies.

Objective: Large Language Models (LLMs) have shown exceptional performance in natural language processing, yet their utility in structured clinical data analysis remains relatively underexplored. This pilot study investigates whether LLM-generated embeddings can preserve the structural integrity of clinical datasets and enhance predictive modeling, particularly in resource-constrained settings. Methods: We applied dimensionality reduction techniques such as Principal Component Analysis (PCA), t-distributed Stochastic Neighbor Embedding (t-SNE), and k-means clustering to compare original data structures with those derived from LLM embeddings. Evaluation metrics included cosine similarity, area under the curve (AUC), and R ² , applied across 100 synthetic datasets and two real-world clinical datasets: the UCI medical database and endocarditis patient records. We assessed multiple LLM architectures, including BERT, RoBERTa, Llama 2, and E5-small, focusing on predictive accuracy and computational efficiency. Results: LLM embeddings closely mirrored original data structures, with BERT achieving a cosine similarity of 0.95 on linear datasets and Llama 2 (30B) reaching 0.85 on quadratic datasets, albeit with higher computational costs. Predictive performance improved significantly across the board with increases in subject variable ratio (SVR), three groups were identified similar performance, assisted better and assisted significantly better. These groups differed based upon the equation used to generate synthetic data. Discussion: These findings highlight the potential of LLMs to enhance structured data analysis by identifying optimal conditions, such as SVR thresholds, for their practical use. The trade-off between computational cost and performance across different LLM architectures is also emphasized, suggesting the need for context-specific model selection. Conclusion: LLMs can be effectively leveraged to repurpose existing clinical datasets for individualized clinical questions, such as optimizing surgical timing for patients with infective endocarditis and embolic stroke. This approach advances precision medicine and supports data-driven clinical decision-making.