aceUsing interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study
Influenza, a prevalent disease, significantly threatens public health. Accurately predicting severe influenza occurrences is crucial for developing personalized prevention strategies and treatment plans. 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. A retrospective cohort study was conducted, co…
A Random Forest model trained on first-24-hour EMR data predicted severe influenza in hospitalized children with AUROC 0.88, outperforming five other models on decision curve analysis, with blood glucose ranked as top predictor.
Evidence
- Peer-reviewedBMC Medical Informatics and Decision Making2026-10-05
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Truvace Impact Record TRV-2026-1293, v1: “Using interpretable machine learning models to predict the occurrence of severe influenza in hospitalized children: a retrospective cohort study.” Truvace, 2026-10-06. /record/TRV-2026-1293 (accessed at citation time). sha256 8834a35cd269437d…
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