Reducing False-Positive Results in Newborn Screening Using Machine Learning

Newborn screening (NBS) for inborn metabolic disorders is a highly successful public health program that by design is accompanied by false-positive results. Here we trained a Random Forest machine learning classifier on screening data to improve prediction of true and false positives. Data included...

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Detalles Bibliográficos
Autores principales: Peng, Gang, Tang, Yishuo, Cowan, Tina M., Enns, Gregory M., Zhao, Hongyu, Scharfe, Curt
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080200/
https://www.ncbi.nlm.nih.gov/pubmed/32190768
http://dx.doi.org/10.3390/ijns6010016

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7080200/
https://www.ncbi.nlm.nih.gov/pubmed/32190768
http://dx.doi.org/10.3390/ijns6010016

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