Introduction:Clinical manifestations are essential for early diagnosis of influenza-like illness(ILI).Machine learning models for influenza prediction were developed and a new ILI definition was introduced.Methods:A r...Introduction:Clinical manifestations are essential for early diagnosis of influenza-like illness(ILI).Machine learning models for influenza prediction were developed and a new ILI definition was introduced.Methods:A retrospective cohort study was conducted at three hospitals in southwest China during June 2022 and May 2023.Artificial intelligence was used to extract variables from medical records and XGBOOST algorithm was used to develop prediction models for the total population and three age subgroups.A new ILI definition was introduced based on the optimal model and its performance was compared with WHO,China CDC,and USA CDC definitions.Results:Totally 200,135 patients were included.4,249(36.2%)were confirmed influenza.The predictors of the optimal model included epidemiological characteristics,important symptoms and signs,and age for the total population[Area under curve(AUC)0.734(0.710–0.750),accuracy 0.689(0.669–0.772)].The new ILI definition was fever(≥37.9℃)with cough or rhinorrhea,and its AUC,sensitivity,and specificity for diagnosing influenza were 0.618(0.598–0.639),0.665 and 0.572,outperformed the WHO,China CDC,and USA CDC definitions(P<0.05).Conclusions:Fever,cough,and rhinorrhea maybe the most important indicators for influenza surveillance.展开更多
基金Supported by the Chongqing Social Science Planning Project(Grant Number 2020PY48)funded by the Chongqing Federation of Social Sciencethe Joint Project of Chongqing Science and Technology Bureau and Health Commission(Grant Number 2020NCPZX03)funded by the Chongqing Science and Technology Bureau and Chongqing Health Commission of China.
文摘Introduction:Clinical manifestations are essential for early diagnosis of influenza-like illness(ILI).Machine learning models for influenza prediction were developed and a new ILI definition was introduced.Methods:A retrospective cohort study was conducted at three hospitals in southwest China during June 2022 and May 2023.Artificial intelligence was used to extract variables from medical records and XGBOOST algorithm was used to develop prediction models for the total population and three age subgroups.A new ILI definition was introduced based on the optimal model and its performance was compared with WHO,China CDC,and USA CDC definitions.Results:Totally 200,135 patients were included.4,249(36.2%)were confirmed influenza.The predictors of the optimal model included epidemiological characteristics,important symptoms and signs,and age for the total population[Area under curve(AUC)0.734(0.710–0.750),accuracy 0.689(0.669–0.772)].The new ILI definition was fever(≥37.9℃)with cough or rhinorrhea,and its AUC,sensitivity,and specificity for diagnosing influenza were 0.618(0.598–0.639),0.665 and 0.572,outperformed the WHO,China CDC,and USA CDC definitions(P<0.05).Conclusions:Fever,cough,and rhinorrhea maybe the most important indicators for influenza surveillance.
