| Title: | Evaluation of GPT-4 for 10-year cardiovascular risk prediction: Insights from the UK Biobank and KoGES data |
| Journal: | iScience |
| Published: | 24 Jan 2024 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/38357664/ |
| DOI: | https://doi.org/10.1016/j.isci.2024.109022 |
| URL: | http://www.cell.com/article/S2589004224002438/pdf |
| Citations: | 4 (4 in last 2 years) as of 8 Aug 2024 |
Publication 8874
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Abstract
Cardiovascular disease (CVD) remains a pressing global health concern. While traditional risk prediction methods such as the Framingham and American College of Cardiology/American Heart Association (ACC/AHA) risk scores have been widely used in the practice, artificial intelligence (AI), especially GPT-4, offers new opportunities. Utilizing large scale of multi-center data from 47,468 UK Biobank participants and 5,718 KoGES participants, this study quantitatively evaluated the predictive capabilities of GPT-4 in comparison with traditional models. Our results suggest that the GPT-based score showed commendably comparable performance in CVD prediction when compared to traditional models (AUROC on UKB: 0.725 for GPT-4, 0.733 for ACC/AHA, 0.728 for Framingham; KoGES: 0.664 for GPT-4, 0.674 for ACC/AHA, 0.675 for Framingham). Even with omission of certain variables, GPT-4's performance was robust, demonstrating its adaptability to data-scarce situations. In conclusion, this study emphasizes the promising role of GPT-4 in predicting CVD risks across varied ethnic datasets, pointing toward its expansive future applications in the medical practice.</p>
7 Authors
- Changho Han
- Dong Won Kim
- Songsoo Kim
- Seng Chan You
- Jin Young Park
- SungA Bae
- Dukyong Yoon
1 Application
| Application ID | Title |
|---|---|
| 85037 | Prediction of drug response using pharmacogenomics-driven machine learning approach |
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