Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening

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SEEK ID: https://publications.h-its.org/publications/1799

DOI: 10.1109/SSCI52147.2023.10371991

Research Groups: Data Mining and Uncertainty Quantification

Publication type: Journal

Journal: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)

Book Title: 2023 IEEE Symposium Series on Computational Intelligence (SSCI)

Publisher: IEEE

Citation: 2023 IEEE Symposium Series on Computational Intelligence (SSCI),pp.1566-1571,IEEE

Date Published: 5th Dec 2023

Registered Mode: by DOI

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

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Marcus Buchwald

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Zaunseder, E., Mütze, U., Garbade, S. F., Haupt, S., Kölker, S., & Heuveline, V. (2023). Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening. In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 1566–1571). IEEE. https://doi.org/10.1109/ssci52147.2023.10371991

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Deep Learning and Explainable Artificial Intelligence for Improving Specificity and Detecting Metabolic Patterns in Newborn Screening

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