A neural computational aid to the diagnosis of acute myocardial infarction
A neural computational aid to the diagnosis of acute myocardial infarction
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DOI：
10.1067/mem.2002.122705
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发表时间：
2002-04-01
影响因子：
6.2
通讯作者：
Hollander, JE
中科院分区：
文献类型：
--
作者：
Baxt, WG;Shofer, FS;Hollander, JE
研究目的：对于因前胸痛到急诊科就诊的成人患者，准确识别急性心肌梗死的存在仍然是一个难题。人工神经网络是一种用于复杂模式识别的强大的非线性统计范例，具有在网络功能所需的某些数据丢失时保持准确性的能力。早期的研究表明，人工神经网络能够准确地识别胸痛患者的急性心肌梗死。然而，这些研究并没有在真实的时间内测量网络性能，此时网络功能所需的大量数据可能不可用。他们也没有使用化学心脏标志物data.Methods：2204名成人患者提出的艾德与前胸痛被用来训练人工神经网络识别急性心肌梗死的存在。仅使用初始患者评价时可用的数据来复制实时患者评价的条件。来自患者病史、体格检查、心电图结果和化学心脏标志物测定的40个变量被用于训练和测试网络。（敏感性94.5%; 95%置信区间（90.6%至97.9%），心肌梗死特异性为95.9%（95%置信区间93.0%至98.5%），尽管平均5%所有患者的网络所需输入数据中有0%至35%的数据缺失。当功能所需的一些数据不可用时，网络准确性和该准确性的维持表明人工神经网络可能是在初始患者评估期间诊断急性心肌梗死的潜在真实的时间辅助。
Study objective: Accurate identification of the presence of acute myocardial infarction in adult patients who present to the emergency department with anterior chest pain remains elusive. The artificial neural network is a powerful nonlinear statistical paradigm for the recognition of complex patterns, with the ability to maintain accuracy when some data required for network function are missing. Earlier studies revealed that the artificial neural network is able to accurately identify acute myocardial infarction in patients experiencing chest pain. However, these studies did,, not measure network performance in real time, when a significant amount of data required for network function may not be available. They also did not use chemical cardiac marker data.Methods: Two thousand two hundred four adult patients presenting to the ED with anterior chest pain were used to train an artificial neural network to recognize the presence of acute myocardial infarction. Only data available at the time of initial patient evaluation were used to replicate the conditions of real-time patient evaluation. Forty variables from patient histories, physical examinations, ECG results, and chemical cardiac marker determinations were used to train and then test the network.Results: The network correctly identified 121 of the 128 patients (sensitivity 94.5%; 95% confidence interval 90.6% to 97.9%) with myocardial infarction at a specificity of 95.9% (95% confidence interval 93.0% to 98.5%), despite the fact that an average of 5% (individual range 0% to 35%) of the input data required by the network were missing on all patients.Conclusion: Network accuracy and the maintenance of that accuracy when some data required for function are unavailable suggest that the artificial neural network may be a potential real time aid to the diagnosis of acute myocardial infarction during initial patient evaluation.