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March 20, 2024Frontiers in PediatricsOpen Access

The significance of machine learning in neonatal screening for inherited metabolic diseases

Xiangchun Yang Shuxia Ding Jianping Zhang Zhuojie Hu Danyan Zhuang Fei Wang Shanshan Wu Changshui Chen Haibo Li

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Abstract

Neonatal screening for inherited metabolic diseases (IMDs) has been revolutionized by tandem mass spectrometry (MS/MS). This study aimed to enhance neonatal screening for IMDs using machine learning (ML) techniques.

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Cite This Study

Yang et al. (2024) studied this question.

synapsesocial.com/papers/68e732d3b6db6435876ac83f https://doi.org/10.3389/fped.2024.1366891

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Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

1Mass spectrometry in newborn and metabolic screening: historical perspective and future directionsOriginal Research · 2008 · 100 citations
2Exploring the technical possibilities of metabolomic versus genomic tests for diagnosis of inborn errors of metabolism in NICUOriginal Research · 2026
3Prediction of inherited metabolic disorders using tandem mass spectrometry data with the help of artificial neural networks2024
4Tandem Mass Spectrometry Screening for Inborn Errors of Metabolism: Comprehensive Bibliometric Analysis2024 · 1 citations
5The new frontier of newborn screening: integrating genomics, metabolomics, and data science for inborn errors of metabolismReview · 2026

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An effective integrated program of neonatal screening - as a modern direction of diagnosis of hereditary metabolic diseases in children (literature review)2024

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