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Schallmoser, Simon 
ORCID: https://orcid.org/0000-0003-4076-0584; Zueger, Thomas ORCID: https://orcid.org/0000-0001-6190-7405; Kraus, Mathias ORCID: https://orcid.org/0000-0002-2021-2743; Saar-Tsechansky, Maytal ORCID: https://orcid.org/0000-0002-2400-3762; Stettler, Christoph ORCID: https://orcid.org/0000-0003-1691-6059 und Feuerriegel, Stefan ORCID: https://orcid.org/0000-0001-7856-8729
(2023):
Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.
In: Journal of Medical Internet Research, Bd. 25, e42181
ORCID: https://orcid.org/0000-0003-4076-0584; Zueger, Thomas ORCID: https://orcid.org/0000-0001-6190-7405; Kraus, Mathias ORCID: https://orcid.org/0000-0002-2021-2743; Saar-Tsechansky, Maytal ORCID: https://orcid.org/0000-0002-2400-3762; Stettler, Christoph ORCID: https://orcid.org/0000-0003-1691-6059 und Feuerriegel, Stefan ORCID: https://orcid.org/0000-0001-7856-8729
(2023):
Machine Learning for Predicting Micro- and Macrovascular Complications in Individuals With Prediabetes or Diabetes: Retrospective Cohort Study.
In: Journal of Medical Internet Research, Bd. 25, e42181
Zueger, Thomas ORCID: https://orcid.org/0000-0001-6190-7405; Schallmoser, Simon; Kraus, Mathias; Saar-Tsechansky, Maytal; Feuerriegel, Stefan ORCID: https://orcid.org/0000-0001-7856-8729 und Stettler, Christoph
(2022):
Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes.
In: Diabetes Technology & Therapeutics, Bd. 24, Nr. 11: S. 842-847
ORCID: https://orcid.org/0000-0001-6190-7405; Schallmoser, Simon; Kraus, Mathias; Saar-Tsechansky, Maytal; Feuerriegel, Stefan ORCID: https://orcid.org/0000-0001-7856-8729 und Stettler, Christoph
(2022):
Machine Learning for Predicting the Risk of Transition from Prediabetes to Diabetes.
In: Diabetes Technology & Therapeutics, Bd. 24, Nr. 11: S. 842-847
Schallmoser, Simon; Krippendorf, Sven; Chadha-Day, Francesca und Weller, Jochen ORCID: https://orcid.org/0000-0002-8282-2010
(2022):
Updated bounds on axion-like particles from X-ray observations.
In: Monthly Notices of the Royal Astronomical Society, Bd. 514, Nr. 1: S. 329-341
ORCID: https://orcid.org/0000-0002-8282-2010
(2022):
Updated bounds on axion-like particles from X-ray observations.
In: Monthly Notices of the Royal Astronomical Society, Bd. 514, Nr. 1: S. 329-341