Accessibility settings

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

New Methods (1176) Clinical Information and Decision Making (2057) Machine Learning (2221) Clinical Informatics (1431) Data Science (385) Alarm Fatigue (26) Tools, Programs and Algorithms (407) Decision Support for Health Professionals (1627) Intensive Care Unit (ICU) (168) Telehealth and Telemonitoring (2192)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/65961, first published .
Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Developing a Scalable Annotation Method for Large Datasets That Enhances Alarms With Actionability Data to Increase Informativeness: Mixed Methods Approach

Authors of this article:

Sophie Anne Inès Klopfenstein1, 2 Author Orcid Image ;   Anne Rike Flint1 Author Orcid Image ;   Patrick Heeren1, 3 Author Orcid Image ;   Mona Prendke1 Author Orcid Image ;   Amin Chaoui1 Author Orcid Image ;   Thomas Ocker3 Author Orcid Image ;   Jonas Chromik4 Author Orcid Image ;   Bert Arnrich4 Author Orcid Image ;   Felix Balzer1, 5 Author Orcid Image ;   Akira-Sebastian Poncette1, 3 Author Orcid Image
Article Authors Cited by (2) Tweetations Metrics Author Video

Journals

  1. Mirzaei A, Pazhouhan M, Jahanbakht M, Eftekhari F. Artificial intelligence across design thinking: a qualitative review. Design Science 2026;12 View
  2. Kraevsky K, Aqtash S, Teh F, Pinsky M, Clermont G, Hravnak M, Al-Zaiti S. A comprehensive cross-sectional study of bedside monitor alarm characteristics and alarm load across hospital units. Scientific Reports 2026 View