Artificial Intelligence in Allergy and Immunology: Comparing Risk Prediction Models to Help Screen Inborn Errors of Immunity

Background: Inborn errors of immunity (IEI) are underdiagnosed disorders, leading to increased morbimortality and expenses for healthcare system. Objectives: The study aimed to develop and compare risk prediction model to measure the individual chance of a confirmed diagnosis of IEI in children at risk for this disorder. Method: Clinical and laboratory data of 128 individuals were used to derive machine learning (ML) and logistic regression risk prediction models, to measure the individual chance of a confirmed diagnosis of IEI in children with suspected disorder, according to previous general pediatrician/clinician judgement. Their performances were compared. Results: Statistically significant variables were mainly leucopenia, neutropenia, lymphopenia, and low levels of immunoglobulins A/G/M. ML models performed better. Conclusion: The enhanced predictive power provided by ML models could be a resource to track IEI, providing better healthcare outcomes.