首页> 美国卫生研究院文献>Scandinavian Journal of Trauma Resuscitation and Emergency Medicine >An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction
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An artificial neural network to safely reduce the number of ambulance ECGs transmitted for physician assessment in a system with prehospital detection of ST elevation myocardial infarction

机译:在院前检测到ST抬高型心肌梗死的系统中一个人工神经网络可以安全地减少传送给医生评估的救护车心电图的数量

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摘要

BackgroundPre-hospital electrocardiogram (ECG) transmission to an expert for interpretation and triage reduces time to acute percutaneous coronary intervention (PCI) in patients with ST elevation Myocardial Infarction (STEMI). In order to detect all STEMI patients, the ECG should be transmitted in all cases of suspected acute cardiac ischemia. The aim of this study was to examine the ability of an artificial neural network (ANN) to safely reduce the number of ECGs transmitted by identifying patients without STEMI and patients not needing acute PCI.
机译:背景技术将院前心电图(ECG)传输给专家进行解释和分诊,可以减少ST抬高型心肌梗死(STEMI)患者的急性经皮冠状动脉介入治疗(PCI)时间。为了检测所有STEMI患者,在所有怀疑的急性心脏缺血病例中均应传播ECG。这项研究的目的是通过识别没有STEMI的患者和不需要急性PCI的患者来检查人工神经网络(ANN)安全减少ECG数量的能力。

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