Predicting first-time anaphylaxis in the elderly using stacked machine learning and population registers.

ABSTRACT:

Background

Anaphylaxis is a severe, potentially life-threatening allergic reaction that requires rapid identification and intervention. Predicting individuals at risk remains a clinical challenge due to its multifactorial nature and variable presentation.

Objective

To develop and evaluate explainable machine learning models that predict the risk of anaphylaxis using routinely collected clinical data.

Methods

We analysed a matched case-control dataset derived from anonymised electronic health records. After applying chi-squared-based feature selection, we trained multiple classification algorithms-including logistic regression, decision trees, random forests, XGBoost, and a stacking ensemble. Model performance was evaluated using AUC, sensitivity, specificity, precisio

SUBMITTER: Mora T 

PROVIDER: S-EPMC12631650 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature