Enabling machine learning models in alarm fatigue research: Creation of a large relevance-annotated oxygen saturation alarm data set
The data set can be a starting point to reduce the number of unnecessary oxygen saturation alarms. For example, it can serve as a training data set for machine learning models that assess future alarms. The algorithm might be re-used to annotate other alarm data sets as well.
Authors
- Anne Rike Flint (ORCID: https://orcid.org/0000-0002-1414-5812)
- Jonas Chromik (ORCID: https://orcid.org/0000-0002-5709-4381)
- Akira-Sebastian Poncette (ORCID: https://orcid.org/0000-0003-4627-7016)
- Mona Prendke (ORCID: https://orcid.org/0000-0003-0602-9691)
- Bert Arnrich (ORCID: https://orcid.org/0000-0001-8380-7667)
Institutions
- Hasso Plattner Institute (DE)
- Humboldt-Universität zu Berlin (DE)
- Charité - Universitätsmedizin Berlin (DE)
Publication Details
- Journal
- Computers in Biology and Medicine
- Published
- 2024-10-24
- DOI
- https://doi.org/10.1016/j.compbiomed.2024.109244
- Citations
- 2
- Primary Topic
- Healthcare Technology and Patient Monitoring
- Type
- article
- Field-Weighted Citation Impact
- 1.15
Funders
- Bundesministerium für Bildung und Forschung