2022
DOI: 10.1002/jmd2.12285
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Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review

Abstract: 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 support NBS. However, most studies only focus on single diseases or specific ML techniques making it difficult to draw conclusions on which methods are best to implement. Therefore, we performed a systematic literature… Show more

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Cited by 20 publications
(26 citation statements)
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References 46 publications
(224 reference statements)
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“…Recent studies using advanced informatics and data mining tools such as machine learning have demonstrated the potential of such approaches (Baker et al, 2019;James et al, 2020;Robertson et al, 2022;Seo et al, 2022;Tan et al, 2020;Zaunseder et al, 2022).…”
Section: Technical and Implementation Challengesmentioning
confidence: 99%
See 1 more Smart Citation
“…Recent studies using advanced informatics and data mining tools such as machine learning have demonstrated the potential of such approaches (Baker et al, 2019;James et al, 2020;Robertson et al, 2022;Seo et al, 2022;Tan et al, 2020;Zaunseder et al, 2022).…”
Section: Technical and Implementation Challengesmentioning
confidence: 99%
“…While reanalysis of the data can potentially be automated, information technology systems would need to be developed to enable long term tracking of participants. Recent studies using advanced informatics and data mining tools such as machine learning have demonstrated the potential of such approaches (Baker et al, 2019; James et al, 2020; Robertson et al, 2022; Seo et al, 2022; Tan et al, 2020; Zaunseder et al, 2022).…”
Section: Technical and Implementation Challengesmentioning
confidence: 99%
“…Utilizing data from multiple sources, this study highlights the need for accurate and severity‐adjusted case definitions and the importance of well‐characterized longitudinally followed patient cohorts. In the future, multi‐omics data and artificial intelligence‐supported diagnostic pathways might help to overcome the current limitations of case definition and risk stratification 77 …”
Section: Lessons Learned From Long‐term Observation Of Newborn Screen...mentioning
confidence: 99%
“…In the future, multi-omics data and artificial intelligencesupported diagnostic pathways might help to overcome the current limitations of case definition and risk stratification. 77…”
Section: The Phenotype Follows a Continuous Spectrummentioning
confidence: 99%
“…In the context of NBS, a variety of supervised ML methods have been applied to NBS data to predict whether or not a newborn suffers from a condition. The methods and their results were summarized in a recent review [ 15 ]. They enabled a reduction of false positive rates and identification of so far unknown metabolic patterns by relying on complex feature combinations instead of predefined single cut-off values.…”
Section: Introductionmentioning
confidence: 99%