Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 1 de 1
Filtrar
Mais filtros

Base de dados
Ano de publicação
Tipo de documento
Intervalo de ano de publicação
1.
Int Arch Allergy Immunol ; 183(11): 1226-1230, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35973410

RESUMO

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.


Assuntos
Inteligência Artificial , Hipersensibilidade , Criança , Humanos , Aprendizado de Máquina , Modelos Logísticos , Hipersensibilidade/diagnóstico , Atenção à Saúde
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA