AI-assisted diagnostic approach for the influenza-like illness in children: decision support system for patients and clinicians.

Influenza-like illnesses (ILI), such as influenza and RSV, pose significant global health burdens, especially in febrile children under 6 years old. Differentiating these from bacterial infections based solely on clinical symptoms is challenging. While PCR tests are reliable, they are costly and time-consuming. An effective predictive tool would help doctors prioritize tests and guide parents on seeking emergency care for their febrile children. We collected data from 2,559 children who visited the hospital for ILI inspections. We developed XGBoost models, comparing nine different machine learning algorithms. Our AI-assisted diagnostic pipeline consists of two stages: Decision Support System for patients (DSS-P): An in-house model using sex, age, symptoms, and medical history to decide on hospital visits. Decision Support System for clinicians (DSS-C): An in-hospital model incorporating breath sound types and Chest X-ray results to determine the necessity of clinical tests. We tested various experimental settings, including the addition of RAT-tested samples and the combined consideration of influenza and RSV. The performance for influenza achieved an Area Under the Curve of 0.749 and 0.776, while RSV achieved 0.907 and 0.924 in DSS-P and DSS-C, respectively. We identified biomarkers, noting that most biomarkers had opposite effects for influenza and RSV. This study developed predictive models for influenza and RSV and explored their underlying mechanisms. An expectation tool to guide doctors in prioritizing tests or assisting parents in deciding on emergency care for their febrile child would be invaluable. Biomarker analysis performed can provide insight on clinical fields.

Supplementary information: The online version contains supplementary material available at 10.1007/s13534-024-00450-8.