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

Zaunseder, Elaine; Haupt, Saskia; Mütze, Ulrike; Garbade, Sven F; Kölker, Stefan; Heuveline, Vincent

Zaunseder, Elaine; Haupt, Saskia; Mütze, Ulrike; Garbade, Sven F; Kölker, Stefan; Heuveline, Vincent.

Afiliación

Zaunseder E; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR) Heidelberg University Heidelberg Germany.
Haupt S; Data Mining and Uncertainty Quantification (DMQ) Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany.
Mütze U; Engineering Mathematics and Computing Lab (EMCL), Interdisciplinary Center for Scientific Computing (IWR) Heidelberg University Heidelberg Germany.
Garbade SF; Data Mining and Uncertainty Quantification (DMQ) Heidelberg Institute for Theoretical Studies (HITS) Heidelberg Germany.
Kölker S; Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine Heidelberg University Hospital Heidelberg Germany.
Heuveline V; Division of Child Neurology and Metabolic Medicine, Center for Child and Adolescent Medicine Heidelberg University Hospital Heidelberg Germany.

JIMD Rep ; 63(3): 250-261, 2022 May.

Article en En | MEDLINE | ID: mdl-35433168

Palabras clave

data mining; data preprocessing; data science; deep learning; machine learning; modeling; neonatal screening; pattern recognition

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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies / Screening_studies / Systematic_reviews Idioma: En Revista: JIMD Rep Año: 2022 Tipo del documento: Article

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