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

ABSTRACT: 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

SUBMITTER: Lee Y 

PROVIDER: S-EPMC11871169 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature