Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases-Weifang City,Shandong Province,China,2020-2023

作     者：Ting Zhang Liuyang Yang Ziliang Fan Xuancheng Hu Jiao Yang Yan Luo Dazhu Huo Xuya Yu Ling Xin Xuan Han Jie Shan Zhongjie Li Weizhong Yang 

作者机构：School of Population Medicine and Public HealthChinese Academy of Medical Sciences(CAMS)&Peking Union Medical College(PUMC)BeijingChina State Key Laboratory of Respiratory Health and MultimorbidityBeijingChina Key Laboratory of Pathogen Infection Prevention and Control(Peking Union Medical College)Ministry of EducationBeijingChina The Third Affiliated Hospital of Kunming Medical UniversityYunnan Cancer HospitalKunming CityYunnan ProvinceChina School of Data ScienceFudan UniversityShanghaiChina Weifang Center for Disease Control and PreventionWeifang CityShandong ProvinceChina School of Health Policy and ManagementChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina 

出 版 物：《China CDC weekly》 (中国疾病预防控制中心周报（英文）)

年 卷 期：2024年第6卷第26期

页      面：635-641,I0010,I0011页

核心收录：

学科分类：1004[医学-公共卫生与预防医学(可授医学、理学学位)] 1002[医学-临床医学] 100201[医学-内科学(含：心血管病、血液病、呼吸系病、消化系病、内分泌与代谢病、肾病、风湿病、传染病)] 100401[医学-流行病与卫生统计学] 10[医学] 

基　　金：Supported by the CAMS Innovation Fund for Medical Sciences(2021-I2M-1-044,2023-I2M-3-011) the National Key Research and Development Program of China(2023YFC2308701) 

主　　题：specificity integrating utilizing 

摘      要：Introduction:Respiratory infectious diseases,such as influenza and coronavirus disease 2019(COVID-19),present significant global public health *** emergence of artificial intelligence(AI)and big data offers opportunities to improve traditional disease surveillance and early warning ***:The study analyzed data from January 2020 to May 2023,comprising influenza-like illness(ILI)statistics,Baidu index,and clinical data from *** methodologies were evaluated:the adaptive dynamic threshold method(ADTM)for dynamic threshold adjustments,the machine learning supervised method(MLSM),and the machine learning unsupervised method(MLUM)utilizing anomaly *** comparison focused on sensitivity,specificity,timeliness,and warning ***:ADTM issued 37 warnings with a sensitivity of 71%and a specificity of 85%.MLSM generated 35 warnings,with a sensitivity of 82%and a specificity of 87%.MLUM produced 63 warnings with a sensitivity of 100%and specificity of 80%.The initial warnings from ADTM and MLUM preceded those from MLSM by five *** Kappa coefficient indicated moderate agreement between the methods,with values ranging from 0.52 to 0.62(P0.05).Discussion:The study explores the comparison between traditional methods and two machine learning approaches for early warning *** emphasizes the validation of machine learning’s reliability and underscores the unique advantages of each ***,it stresses the significance of integrating machine learning models with various data sources to enhance public health preparedness and response,alongside acknowledging limitations and the need for broader validation.