BMC medical informatics and decision making
Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study
By Wu J, Qian W, Xie S and 12 others.
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.