Opportunities and challenges in machine learning‐based newborn screening—A systematic literature review-Reference-Cited by-同舟云学术

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

Published:2022-03-23 Issue:3 Volume:63 Page:250-261
ISSN:2192-8312
Container-title:JIMD Reports
language:en
Short-container-title:JIMD Reports

Author:

Zaunseder Elaine¹²^ORCID,Haupt Saskia¹²,Mütze Ulrike³,Garbade Sven F.³,Kölker Stefan³,Heuveline Vincent¹²

Affiliation:

1. Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR) Heidelberg University Heidelberg Germany

2. Data Mining and Uncertainty Quantification (DMQ) Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany

3. Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine Heidelberg University Hospital Heidelberg Germany

Funder

Dietmar Hopp Stiftung

Klaus Tschira Stiftung

Publisher

Wiley

Subject

Biochemistry, Genetics and Molecular Biology (miscellaneous),Endocrinology, Diabetes and Metabolism,Internal Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/jmd2.12285

Reference39 articles.

1. Screening Newborns for Inborn Errors of Metabolism by Tandem Mass Spectrometry

2. Expanded Newborn Screening for Inborn Errors of Metabolism by Electrospray Ionization-Tandem Mass Spectrometry: Results, Outcome, and Implications

3. Neonatal Screening in Europe Revisited: An ISNS Perspective on the Current State and Developments Since 2010

4. Current status of newborn screening worldwide: 2015

5. WilsonJMG JungnerG.Principles and practice of screening for disease.WHO Public Health Papers.1968;34. Accessed February 2 2022.https://apps.who.int/iris/handle/10665/37650

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