Article

Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening Program

2023-08-21

Abstract excerpt

This paper presents a methodology for developing a predictive model using random forests to identify true positive cases of cystic fibrosis in neonatal screening, aiming to improve early detection and care for patients. The current heel prick test used in Brazilian neonatal screening has a high incidence of false positives, leading to unnecessary anxiety and medical interventions for patients and their families. O...

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Literature Corpus work: b5d1d381-80f2-5e37-90bc-a8e5358b0c46
DOI: 10.21203/rs.3.rs-3263458/v1

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Newborn Cystic Fibrosis Diagnosis Made Accurate and Efficient with Machine Learning to Reduce False Positives in IRT-Trypsinogen Immunoreactive Screening ProgramDOI 10.21203/rs.3.rs-3263458/v1Read

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