Identifying Kidney Stone Risk Factors Through Patient Experiences With a Large Language Model: Text Analysis and Empirical Study
Comments from social media users offer a new data source for disease prevention and understanding patient journeys. Our method not only sheds light on using LLMs to efficiently summarize risk factors from social media data but also on LLMs' potential to identify new potential factors from the patient's perspective.
Authors
- Rong Chen (ORCID: https://orcid.org/0000-0001-5811-7883)
- Quanjing Zhu (ORCID: https://orcid.org/0000-0003-4813-0894)
- Jiaxuan Li (ORCID: https://orcid.org/0000-0001-7647-6839)
- Patrick Cheong‐Iao Pang (ORCID: https://orcid.org/0000-0002-8820-5443)
- Chao Mao (ORCID: https://orcid.org/0009-0008-0319-4687)
Institutions
- Sun Yat-sen University (CN)
- Sichuan University (CN)
- West China Hospital of Sichuan University (CN)
- The First Affiliated Hospital, Sun Yat-sen University (CN)
- Macao Polytechnic University (MO)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-22
- DOI
- https://doi.org/10.2196/66365
- Citations
- 3
- Primary Topic
- Folate and B Vitamins Research
- Type
- article
- Field-Weighted Citation Impact
- 5.00