Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study.

Abstract

Background: Influenza, a prevalent disease, significantly threatens public health. Accurately predicting severe influenza occurrences is crucial for developing personalized prevention strategies and treatment plans.

Aims: This study aimed to construct a highly interpretable model to assess the risk of severe influenza in hospitalized children, using the SHapley Additive exPlanation (SHAP) method to interpret the Random Forest (RF) model and identify risk factors for severe influenza.

Methods: A retrospective cohort study was conducted, collecting hospitalization records of influenza patients from the Electronic Medical Records (EMR) system for model development and performance evaluation. Data were collected within the first 24 h after patient admission and the dataset was randomly divided into two parts: 70% for model training and 30% for model accuracy verification.

Results: A total of 436 eligible influenza patients were included in the study analysis. The RF model demonstrated the highest predictive accuracy among the six models compared, with an area under the receiver-operating-characteristiccurve(AUROC) of 0.88. The decision curve analysis (DCA) indicated that the net benefit of the RF model surpassed that of other machine learning models. According to the importance ranking by the SHAP method, blood glucose was identified as the most significant predictive variable.

Conclusion: The study established a RF model capable of predicting severe influenza occurrence in hospitalized children. The model, interpreted through the SHAP method, assists physicians in providing better treatment plans.