Prospective validation of artificial neural network trained to identify acute myocardial infarction
Prospective validation of artificial neural network trained to identify acute myocardial infarction
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DOI：
10.1016/s0140-6736(96)91555-x
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发表时间：
1996-01-06
期刊：
影响因子：
168.9
通讯作者：
Skora, J
中科院分区：
文献类型：
--
作者：
Baxt, WG;Skora, J
背景人工神经网络将非线性统计应用于模式识别问题。其中一个这样的问题是急性心肌梗死(AMI)，这是一种诊断，在患者表现为紧急情况时，可能很难确认。方法急诊科医生评估了1070名年龄在18岁或以上的、在美国加利福尼亚州一家教学医院的急诊科就诊的前胸痛患者，并指出他们是否认为这些患者患有心肌梗死。结果发现，医生对心肌梗死诊断的敏感性和特异性分别为73.3%(95%可信区间63.3-83.3%)和81.1%(78.7-83.5%)，网络对心肌梗死的诊断敏感性和特异性分别为96.0%(91.2-100%)和96.0%(94.8-97.2%)。只有7%的患者有急性心肌梗死，这是一种低频率但典型的前胸痛。解释通过人工神经网络的非线性神经计算分析在心肌梗死临床诊断中的应用显示出巨大的潜力。
Background Artificial neural networks apply non-linear statistics to pattern recognition problems. One such problem is acute myocardial infarction (AMI), a diagnosis which, in a patient presenting as an emergency, can be difficult to confirm. We report here a prospective comparison of the diagnostic accuracy of a network and that of physicians, on the same patients with suspected AMI.Methods Emergency department physicians who evaluated 1070 patients 18 years or older presenting to the emergency department of a teaching hospital in California, USA with anterior chest pain indicated whether they thought these patients had sustained a myocardial infarction. The network analysed the patient data collected by the physicians during their evaluations and also generated a diagnosis.Findings The physicians had a diagnostic sensitivity and specificity for myocardial infarction of 73.3% (95% confidence interval 63.3-83.3%) and 81.1% (78.7-83.5%), respectively, while the network had a diagnostic sensitivity and specificity of 96.0% (91.2-100%) and 96.0% (94.8-97.2%), respectively. Only 7% of patients had had an AMI, a low frequency but typical for anterior chest pain.Interpretation The application of non-linear neural computational analysis via an artificial neural network to the clinical diagnosis of myocardial infarction appears to have significant potential.