Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics
The screening model excels in automating prompt identification of RA in primary healthcare, improving the early detection of RA, and reducing delays and associated costs. Our findings contribute positively and are poised to elevate prospective RA management, fostering improvements in healthcare sector responsiveness and resource efficiency.
Authors
- Wenqi Wu (ORCID: https://orcid.org/0000-0001-5985-2804)
- Linyang Yan
- Xiaohao Hu
- Ying‐Qian Mo (ORCID: https://orcid.org/0000-0001-9617-8927)
- Chun Li (ORCID: https://orcid.org/0000-0001-7981-7184)
- Qingwen Wang (ORCID: https://orcid.org/0000-0001-5988-9922)
- Yalin Wu (ORCID: https://orcid.org/0000-0003-2077-4225)
- Zhiyin Li (ORCID: https://orcid.org/0000-0002-7900-4826)
- Xinpeng Chen (ORCID: https://orcid.org/0009-0009-0732-6066)
- H. Zeng (ORCID: https://orcid.org/0000-0002-2032-442X)
- Z. Lin (ORCID: https://orcid.org/0000-0001-5107-8420)
- Bo Li (ORCID: https://orcid.org/0009-0001-2833-2125)
Institutions
- Sun Yat-sen University (CN)
- City University of Hong Kong (HK)
- Hong Kong University of Science and Technology (HK)
- Peking University (CN)
- Sun Yat-sen Memorial Hospital (CN)
- ShenZhen People’s Hospital (CN)
- Peking University People's Hospital (CN)
- Peking University Shenzhen Hospital (CN)
- University of Hong Kong - Shenzhen Hospital (CN)
Publication Details
- Journal
- Journal of Inflammation Research
- Published
- 2025-02-01
- DOI
- https://doi.org/10.2147/jir.s487595
- Citations
- 2
- Primary Topic
- Rheumatoid Arthritis Research and Therapies
- Type
- article
- Field-Weighted Citation Impact
- 3.34
Funders
- National Natural Science Foundation of China
- Science, Technology and Innovation Commission of Shenzhen Municipality