Development and Validation of a Cost-Effective Machine Learning Model for Screening Potential Rheumatoid Arthritis in Primary Healthcare Clinics by Wu W, Hu X, Yan L, Li Z, Li B, Chen X, Lin Z, Zeng H, Li C, Mo Y, Wu Y, Wang Q

“…Wenqi Wu,1,2,* Xiaohao Hu,1,2,* Linyang Yan,3,* Zhiyin Li,4 Bo Li,5 Xinpeng Chen,6 Zexun Lin,7 Huiqiong Zeng,8 Chun Li,9 Yingqian Mo,10 Yalin Wu,3 Qingwen Wang1,2 1Department of Rheumatology and Immunology, Peking University Shenzhen Hospital, Shenzhen, People’s Republic of China; 2Shenzhen Key Laboratory of Inflammatory and Immunology Diseases, Shenzhen, People’s Republic of China; 3Department of Ultrasound, Institute of Ultrasonic Medicine, Peking University Shenzhen Hospital, Shenzhen Peking University-The Hong Kong University of Science and Technology Medical Center, Shenzhen, 518036, People’s Republic of China; 4Department of Information Systems, City University of Hong Kong, Hong Kong, People’s Republic of China; 5Department of Rheumatology and Immunology, People’s Hospital of Longhua District, Shenzhen, People’s Republic of China; 6Department of Rheumatology and Immunology, Shenzhen Futian Hospital for Rheumatic Diseases, Shenzhen, People’s Republic of China; 7Shenzhen Nanshan Medical Group HQ Taohuayuan Community Health Service Center, Shenzhen, People’s Republic of China; 8Traditional Chinese Medicine Department of Rheumatism, Women & Children Health Institute, Shenzhen, People’s Republic of China; 9Department of Rheumatology and Immunology, Peking University People’s Hospital, Beijing, People’s Republic of China; 10Department of Rheumatology and Immunology, Sun Yat-Sen Memorial Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yalin Wu; Qingwen Wang, Email yalinwu@jj.ac.kr; wangqingwen@pkuszh.comObjective: In primary healthcare, diagnosing rheumatoid arthritis (RA) is challenging due to a general lack of in-depth knowledge of RA by general practitioners (GPs) and the lack of effective tools, leading to high rates of missed diagnosis. …”
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