Kennedy R L, Harrison R F, Burton A M, Fraser H S, Hamer W G, MacArthur D, McAllum R, Steedman D J
City Hospitals Sunderland, Department of Medicine, UK.
Comput Methods Programs Biomed. 1997 Feb;52(2):93-103. doi: 10.1016/s0169-2607(96)01782-8.
Recent studies have confirmed that artificial neural networks (ANNs) are adept at recognising patterns in sets of clinical data. The diagnosis of acute myocardial infarction (AMI) in patients presenting with chest pain remains one of the greatest challenges in emergency medicine. The aim of this study was to evaluate the performance of an ANN trained to analyse clinical data from chest pain patients. The ANN was compared with serum myoglobin measurements--cardiac damage is associated with increased circulating myoglobin levels, and this is widely used as an early marker for evolving AMI. We used 39 items of clinical and ECG data from the time of presentation to derive 53 binary inputs to a back propagation network. On test data (200 cases), overall accuracy, sensitivity, specificity and positive predictive value (PPV) of the ANN were 91.8, 91.2, 90.2 and 84.9% respectively. Corresponding figures using linear discriminant analysis were 81.0, 77.9, 82.6 and 69.7% (P < 0.01). Using a further test set from a different centre (91 cases), the accuracy, sensitivity, specificity and PPV for the admitting physicians were 65.1, 28.5, 76.9 and 28.6% respectively compared with 73.6, 52.4, 80.0 and 44.0% for the ANN. Although myoglobin at presentation was highly specific, it was only 38.0% sensitive, compared with 85.7% at 3 h. Simple strategies to combine clinical opinion, ANN output and myoglobin at presentation could greatly improve sensitivity and specificity of AMI diagnosis. The ideal support for emergency room physicians may come from a combination of computer-aided analysis of clinical factors and biochemical markers such as myoglobin. This study demonstrates that the two approaches could be usefully combined, the major benefit of the decision support system being in the first 3 h before biochemical markers have become abnormal.
最近的研究证实，人工神经网络（ANNs）擅长识别临床数据集中的模式。对胸痛患者进行急性心肌梗死（AMI）的诊断仍然是急诊医学中最大的挑战之一。本研究的目的是评估一个经过训练以分析胸痛患者临床数据的人工神经网络的性能。将该人工神经网络与血清肌红蛋白测量结果进行比较——心脏损伤与循环肌红蛋白水平升高有关，这被广泛用作急性心肌梗死进展的早期标志物。我们使用了从就诊时起的39项临床和心电图数据，为反向传播网络得出53个二元输入。在测试数据（200例）上，人工神经网络的总体准确率、敏感性、特异性和阳性预测值（PPV）分别为91.8%、91.2%、90.2%和84.9%。使用线性判别分析的相应数字分别为81.0%、77.9%、82.6%和69.7%（P<0.01）。使用来自不同中心的另一组测试数据（91例），主治医生的准确率、敏感性、特异性和PPV分别为65.1%、28.5%、76.9%和28.6%，而人工神经网络的相应数字分别为73.6%、52.4%、80.0%和44.0%。尽管就诊时的肌红蛋白具有高度特异性，但它的敏感性仅为38.0%，而3小时时为85.7%。将临床意见、人工神经网络输出和就诊时的肌红蛋白相结合的简单策略可以大大提高AMI诊断的敏感性和特异性。对急诊室医生的理想支持可能来自临床因素的计算机辅助分析和肌红蛋白等生化标志物的结合。这项研究表明，这两种方法可以有效地结合，决策支持系统的主要益处在于生化标志物出现异常之前的前3小时。
