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). 2023 IEEE Symposium Series on Computational Intelligence (SSCI). IEEE. https://doi.org/10.1109/ssci52147.2023.10371991

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Created: 16th Feb 2024 at 12:47

Last updated: 5th Mar 2024 at 21:25

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

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