Institute of Medical Informatics, Charité - Universitätsmedizin Berlin, Germany.
Berlin Institute of Health, Charité - Universitätsmedizin Berlin, Germany.
Stud Health Technol Inform. 2022 May 25;294:273-274. doi: 10.3233/SHTI220453.
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.
报警有助于检测重症监护病房中的医疗状况，提高患者安全性。然而，高达 99%的报警是不可操作的，即报警在规定的时间内没有触发医疗干预。通过机器学习 (ML) 减少其数量被假设为一种有前途的方法，可以改善患者监测和报警管理。本回顾性研究介绍了将报警标记为可操作和不可操作的技术和医学预处理步骤，为 ML 应用程序创建了基础。
