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

Autor:	Takao MMV; Pediatric Allergy and Immunology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil, mm.takao@yahoo.com.br., Carvalho LSF; Data Lab, Clarity Healthcare Intelligence, Jundiaí, Brazil.; Laboratory of Data for Quality of Care and Outcomes Research, Institute for Strategic Management in Healthcare DF (IGESDF), Brasília, Brazil.; Cardiology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil., Silva PGP; Pediatric Allergy and Immunology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil., Pereira MM; Pediatric Allergy and Immunology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil., Viana AC; Faculty of Medical Sciences/Pontifical Catholic University of Campinas (PUC-Campinas), Campinas, Brazil., da Silva MTN; Pediatric Allergy and Immunology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil.; Pediatric Research Center (CIPED), Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil., Riccetto AGL; Pediatric Allergy and Immunology Division, Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil.; Pediatric Research Center (CIPED), Faculty of Medical Sciences/State University of Campinas (Unicamp), Campinas, Brazil.
Jazyk:	angličtina
Zdroj:	International archives of allergy and immunology [Int Arch Allergy Immunol] 2022; Vol. 183 (11), pp. 1226-1230. Date of Electronic Publication: 2022 Aug 16.
DOI:	10.1159/000526204
Abstrakt:	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. (© 2022 S. Karger AG, Basel.)
Databáze:	MEDLINE
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