ChatGPT-Assisted Deep Learning Models for Influenza-Like Illness Prediction in Mainland China: Time Series Analysis
Deep learning models, particularly TiDE, demonstrate strong potential for accurate ILI forecasting across diverse regions of China. Furthermore, large language models like ChatGPT can substantially enhance modeling efficiency and accessibility by assisting nontechnical users in model development. These findings support the integration of AI-assisted workflows into epidemic prediction systems as a scalable approach for improving public health preparedness.
Authors
- Xiaotao He (ORCID: https://orcid.org/0009-0002-3622-0358)
- Jingzhen Lai (ORCID: https://orcid.org/0000-0003-2054-6396)
- Shiyi Lai (ORCID: https://orcid.org/0000-0003-0049-8155)
- Junjun Jiang (ORCID: https://orcid.org/0000-0003-4905-9348)
- Wudi Wei (ORCID: https://orcid.org/0000-0001-6703-4529)
- Baili Zhan (ORCID: https://orcid.org/0009-0007-7639-7824)
- Rongfeng Chen (ORCID: https://orcid.org/0000-0002-6704-8530)
- Hao Liang (ORCID: https://orcid.org/0000-0001-7534-5124)
- Weihong Huang (ORCID: https://orcid.org/0000-0002-7168-4943)
- Li Ye (ORCID: https://orcid.org/0000-0001-7688-4867)
- Xiaoting Xie (ORCID: https://orcid.org/0000-0002-3779-4786)
- Zongxiang Yuan
- Meng Zhang (ORCID: https://orcid.org/0009-0004-5279-5164)
Institutions
- Guangxi Medical University (CN)
- Guangxi Center for Disease Prevention and Control (CN)
Publication Details
- Journal
- Journal of Medical Internet Research
- Published
- 2025-05-08
- DOI
- https://doi.org/10.2196/74423
- Citations
- 6
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 3.93