{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,29]],"date-time":"2025-08-29T16:10:19Z","timestamp":1756483819305,"version":"3.44.0"},"reference-count":35,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":7,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Immunity Inflam &amp;amp; Disease"],"published-print":{"date-parts":[[2025,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:sec><jats:title>Objectives<\/jats:title><jats:p>In the aftermath of the COVID\u201019 pandemic, China witnessed a surge in respiratory virus infections, which presented considerable challenges to primary health care systems. This study developed an interpretable prediction model using complete blood count (CBC) test data. This model aims to identify common respiratory virus infections in patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study's derivation cohort included 7471 patients who presented with fever at Central Hospital between November and December 2023. Each patient underwent diagnostic procedures, including influenza A (Flu A) and <jats:italic>Mycoplasma pneumoniae<\/jats:italic> (MP) antibody testing and CBC. On the basis of the results of the CBC and patients' basic information, modelling and prediction through machine learning (ML) were performed, and external verification was conducted.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Among the developed models, we constructed two distinct versions of the three\u2010class model: one emphasizing high recall and the other balancing precision and recall. The final model was refined through manual parameter adjustments and a comprehensive network search. The high\u2010recall model demonstrated superior performance in detecting Flu A, with a recall rate of 81.0%. Conversely, the precision\u2012recall balanced model exhibited enhanced accuracy in identifying MP cases, with a precision rate of 84.3%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>Our interpretable ML model not only achieves accurate identification of Flu A and MP infections in febrile patients but also addresses the prevalent \u201cblack box\u201d concerns associated with ML techniques. This technique can aid clinicians in enhancing diagnostic efficiency and accuracy. Therefore, this improvement can lead to reduced medical expenses by minimizing unnecessary tests and treatments.<\/jats:p><\/jats:sec>","DOI":"10.1002\/iid3.70237","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T09:56:27Z","timestamp":1754646987000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Clinical Characteristics of Patients With Respiratory Infections After Nonpharmacological Interventions for COVID\u201019 in China Have Ended: Using Machine Learning Approaches to Support Pathogen Prediction at Admission"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-3882-7132","authenticated-orcid":false,"given":"Tian\u2010ning","family":"Li","sequence":"first","affiliation":[{"name":"Department of Clinical Lab Tianjin First Central Hospital Tianjin 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