Toward accurate prediction of pediatric epidemic disease patient volume in the Chaoshan region: A deep learning framework

Accurate prediction of pediatric epidemic infectious diseases is critical for effective prevention and personalized treatment. Herein, we developed a deep learning framework for the epidemiological characteristics of the Chaoshan region, using electronic health records data from 278,506 pediatric outpatient and emergency visits at the Second Affiliated Hospital of Shantou University Medical College between 2017 and 2023. Our framework is designed to learn pediatric representations that capture local epidemic dynamics and to meet regional clinical prediction needs. Results demonstrate that the framework achieves strong predictive performance on the regional dataset. Our framework yields at least a 6.12% improvement over its counterparts in terms of average correlation coefficient; it achieves the lowest errors in both root-mean-square error (RMSE = 0.130) and mean absolute scaled error (MASE = 0.610). Our framework provided targeted decision support for local healthcare institutions in workforce allocation, medication, and supply planning, thereby contributing to improved prevention strategies.