Prediction Modeling

Performance gains in prospective spending prediction came from both XGBoost methodology and contextual features. The study compared patient-only, system-context, and full-context nested feature sets. No included paediatric prediction model used machine learning or deep learning. Model performance was assessed using discrimination and calibration metrics including AUC, calibration slope, calibration-in-the-large, expected-to-observed ratio, and calibration plots. The models require external validation before broader use beyond the studied setting. The Lebanese depression model performed well with AUC 0.81 and near-perfect expected-to-observed ratio. The diagnostic prediction models included between 81 and 1,515 children and between 10 and 125 invasive fungal infection events. Nine included studies developed ten diagnostic prediction models for invasive fungal infection in children. Model performance was evaluated on a held-out test set using outcome-specific metrics. Although modest in absolute terms, the anticipated AUC increment of 0.03 is considered clinically meaningful and aligns with significant improvements in other prediction metrics observed in prior work. The pre-specifie…