Prediction of peanut oral food challenge outcomes using machine learning

Food allergy is a severe public health problem. Food-specific skin and immunoglobulin E (IgE) tests provide limited diagnostic accuracy. Confirmatory oral food challenges (OFCs) are often required for diagnosis, but barriers hamper broad use. We utilized advanced machine learning methodologies to evaluate whether predictive models based on commonly-available clinical variables could outperform purely statistical methods for OFCs.

Source: Journal of Allergy and Clinical Immunology - February 1, 2024 Category: Allergy & Immunology Authors: Charles Schuler, Justin Zhang, Kayvan Najarian, Rajan Ravikumar, Georgiana Sanders, Jonathan Gryak Source Type: research