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This paper is in the following e-collection/theme issue:

Patient Experiences with Diabetes Technology (63) Clinical Information and Decision Making (3686) Glucose Tracking and Self-Monitoring of Blood Glucose (240) Apps, Mobile, Wearables for Diabetes (442) Sensors for Glucose Monitoring and Diabetes Management (69) Machine Learning (3159)

Published on in Vol 10 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/67867, first published .
Person using a lancet to prick finger for blood glucose monitoring.

Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study

Early Detection of Elevated Ketone Bodies in Type 1 Diabetes Using Insulin and Glucose Dynamics Across Age Groups: Model Development Study

Authors of this article:

Simon Cichosz1 Author Orcid Image ;   Clara Bender1 Author Orcid Image
Article Authors Cited by (3) Tweetations Metrics

Journals

  1. Ghaderi M, Chitzan-Zadeh K, Mokarami M, Mokarami M, Shokoohi M, Vafa R. Revolutionizing diabetes management: Artificial intelligence (AI) from assessment to advanced monitoring. Next Research 2025;2(3):100472 View
  2. Bergenstal R, Virdi N, Quadri F, Alva S. Monitoring Ketonemia in People with Diabetes: Preliminary Findings from Real-World Studies. Diabetes Technology & Therapeutics 2025;27(4_suppl) View
  3. Andersen A, Mehdizavareh H, Khan A, Becher T, Britsch S, Britsch M, Bøttcher M, Winther S, Rohde P, Jensen M, Cichosz S. Tipping the balance: impact of class imbalance correction on the performance of clinical risk prediction models. Journal of the American Medical Informatics Association 2026 View