{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["6(26)"],"submitter":["Zhang T"],"pubmed_abstract":["<h4>Introduction</h4>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.<h4>Methods</h4>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 warn"],"journal":["China CDC weekly"],"pagination":["635-641"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11219296"],"repository":["biostudies-literature"],"pubmed_title":["Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023."],"pmcid":["PMC11219296"],"pubmed_authors":["Xin L","Yang J","Yang L","Fan Z","Shan J","Hu X","Han X","Huo D","Yang W","Zhang T","Li Z","Luo Y","Yu X"],"additional_accession":[]},"is_claimable":false,"name":"Comparison Between Threshold Method and Artificial Intelligence Approaches for Early Warning of Respiratory Infectious Diseases - Weifang City, Shandong Province, China, 2020-2023.","description":"<h4>Introduction</h4>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.<h4>Methods</h4>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 warn","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Jun","modification":"2025-04-04T12:54:38.338Z","creation":"2025-04-04T12:54:38.338Z"},"accession":"S-EPMC11219296","cross_references":{"pubmed":["38966311"],"doi":["10.46234/ccdcw2024.119"]}}