PDF(Pubmed)
Abstract：
UNASSIGNED: Respiratory infectious diseases, such as influenza and coronavirus disease 2019 (COVID-19), present significant global public health challenges. The emergence of artificial intelligence (AI) and big data offers opportunities to improve traditional disease surveillance and early warning systems.
UNASSIGNED: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three 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 detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency.
UNASSIGNED: 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 days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05).
UNASSIGNED: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning\'s reliability and underscores the unique advantages of each method. Furthermore, 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.
UNASSIGNED: The study analyzed data from January 2020 to May 2023, comprising influenza-like illness (ILI) statistics, Baidu index, and clinical data from Weifang. Three 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 detection. The comparison focused on sensitivity, specificity, timeliness, and warning consistency.
UNASSIGNED: 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 days. The Kappa coefficient indicated moderate agreement between the methods, with values ranging from 0.52 to 0.62 (P<0.05).
UNASSIGNED: The study explores the comparison between traditional methods and two machine learning approaches for early warning systems. It emphasizes the validation of machine learning\'s reliability and underscores the unique advantages of each method. Furthermore, 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.
摘要：
■呼吸道传染病，如2019年流感和冠状病毒病（COVID-19），面临重大的全球公共卫生挑战。人工智能（AI）和大数据的出现为改善传统的疾病监测和预警系统提供了机会。
■该研究分析了2020年1月至2023年5月的数据，包括流感样疾病（ILI）统计数据。百度指数，和潍坊的临床资料。评估了三种方法：用于动态阈值调整的自适应动态阈值方法（ADTM），机器学习监督方法(MLSM)，以及利用异常检测的机器学习无监督方法(MLUM)。比较集中在灵敏度上，特异性，及时性、及时性警告的一致性。
■ADTM发出了37次警告，灵敏度为71％，特异性为85％。MLSM生成了35个警告，灵敏度为82%，特异性为87%。MLUM产生了63个警告，敏感性为100％，特异性为80％。ADTM和MLUM的最初警告比MLSM的警告早了五天。Kappa系数表明方法之间有适度的一致性，取值范围为0.52~0.62(P<0.05)。
■该研究探讨了传统方法与两种用于预警系统的机器学习方法之间的比较。它强调了机器学习可靠性的验证，并强调了每种方法的独特优势。此外，它强调了将机器学习模型与各种数据源集成在一起以增强公共卫生准备和响应的重要性，同时承认局限性和需要更广泛的验证。
■该研究分析了2020年1月至2023年5月的数据，包括流感样疾病（ILI）统计数据。百度指数，和潍坊的临床资料。评估了三种方法：用于动态阈值调整的自适应动态阈值方法（ADTM），机器学习监督方法(MLSM)，以及利用异常检测的机器学习无监督方法(MLUM)。比较集中在灵敏度上，特异性，及时性、及时性警告的一致性。
■ADTM发出了37次警告，灵敏度为71％，特异性为85％。MLSM生成了35个警告，灵敏度为82%，特异性为87%。MLUM产生了63个警告，敏感性为100％，特异性为80％。ADTM和MLUM的最初警告比MLSM的警告早了五天。Kappa系数表明方法之间有适度的一致性，取值范围为0.52~0.62(P<0.05)。
■该研究探讨了传统方法与两种用于预警系统的机器学习方法之间的比较。它强调了机器学习可靠性的验证，并强调了每种方法的独特优势。此外，它强调了将机器学习模型与各种数据源集成在一起以增强公共卫生准备和响应的重要性，同时承认局限性和需要更广泛的验证。
