Prediction of pediatric peanut oral food challenge outcomes using machine learning

• Published in: The journal of allergy and clinical immunology. Global Vol. 3; no. 3; p. 100252
• Main Authors: Gryak, Jonathan, Georgievska, Aleksandra, Zhang, Justin, Najarian, Kayvan, Ravikumar, Rajan, Sanders, Georgiana, Schuler, Charles F.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.08.2024; Elsevier
• Subjects: anaphylaxis; food allergy; machine learning; oral food challenges; Original article; Peanut allergy; SHAP; LR; anaphylaxis; LEAP; machine learning; food allergy; OFC; oral food challenges; AUC; Ara h; LUCCK; Peanut allergy; SPT; UM; FA

Clinical testing, including food-specific skin and serum IgE level tests, provides limited accuracy to predict food allergy. Confirmatory oral food challenges (OFCs) are often required, but the associated risks, cost, and logistic difficulties comprise a barrier to proper diagnosis. We sought to uti...