Opportunities and challenges in machine learning-based newborn screening—A systematic literature review

Opportunities and challenges in machine learning-based newborn screening—A systematic literature review

The development and continuous optimization of newborn screening (NBS) programs remains an important and challenging task due to the low prevalence of screened diseases and high sensitivity requirements for screening methods. Recently, different machine learning (ML) methods have been applied to sup...

Full description

Bibliographic Details
Main Authors: Garbade, S.F (Author), Haupt, S. (Author), Heuveline, V. (Author), Kölker, S. (Author), Mütze, U. (Author), Zaunseder, E. (Author)
Format: Article
Language:English
Published: John Wiley and Sons Inc 2022
Subjects:
Article
artificial intelligence
artificial neural network
classification algorithm
data mining
data preprocessing
data science
deep learning
discriminant analysis
human
learning algorithm
machine learning
modeling
multiple acyl CoA dehydrogenase deficiency
neonatal screening
newborn screening
pattern recognition
phenylketonuria
predictive value
prevalence
publication
receiver operating characteristic
risk factor
sensitivity and specificity
support vector machine
systematic review
transcriptomics
Online Access:View Fulltext in Publisher

Internet

View Fulltext in Publisher

Similar Items