| Autor: |
Anne Rike, Flint, Sophie A I, Klopfenstein, Patrick, Heeren, Felix, Balzer, Akira-Sebastian, Poncette |
| Rok vydání: |
2022 |
| Předmět: |
|
| Zdroj: |
Studies in health technology and informatics. 294 |
| ISSN: |
1879-8365 |
| Popis: |
Alarms help to detect medical conditions in intensive care units and improve patient safety. However, up to 99% of alarms are non-actionable, i.e. alarm that did not trigger a medical intervention in a defined time frame. Reducing their amount through machine learning (ML) is hypothesized to be a promising approach to improve patient monitoring and alarm management. This retrospective study presents the technical and medical pre-processing steps to annotate alarms into actionable and non-actionable, creating a basis for ML applications. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
|
