Predicting survival of patients with deep burns using supervised machine learning algorithms

Predicting survival of patients with deep burns using supervised machine learning algorithms. Download PDF Abstract Deep burns carry a high risk of death and early reliable mortality prediction can support triage, resource allocation, and clinical decision-making. We aimed to develop and evaluate a leakage-resistant Machine Learning (ML) model to predict in-hospital mortality in patients with deep burns using only information available within the first 24 h of admission.

In this retrospective, single-center study of 528 patients with deep (third-degree) burns (240 in-hospital deaths, 45.5%), twenty-four first-24-hour predictors were analyzed. Ten supervised ML algorithms were compared using nested 10 × 5 cross-validation, with all preprocessing performed within each fold to prevent leakage. A final untouched 20% hold-out set, derived from the same center and period, was used for evaluation.

Class imbalance was addressed with balanced class weights, and missingness not at random was modeled explicitly. We ultimately selected a regularized logistic regression model, as it combined the highest cross-validated discrimination (area under the receiver-operating-characteristic curve (AUROC) 0.982 ± 0.020) with excellent calibration and full interpretability. On the hold-out set, the model remained solid, achieving an AUROC of 0.992 (95% CI: 0.980–0.999) and a Brier score of 0.04.

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