NEURAL NETWORK ANALYSIS OF SERIAL CARDIAC ENZYME DATA - A CLINICAL-APPLICATION OF ARTIFICIAL MACHINE INTELLIGENCE
NEURAL NETWORK ANALYSIS OF SERIAL CARDIAC ENZYME DATA - A CLINICAL-APPLICATION OF ARTIFICIAL MACHINE INTELLIGENCE
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DOI：
10.1093/ajcp/96.1.134
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发表时间：
1991-07-01
影响因子：
3.5
通讯作者：
HEINSIMER, JA
中科院分区：
文献类型：
--
作者：
FURLONG, JW;DUPUY, ME;HEINSIMER, JA
最近，在人工智能的广泛领域中，对计算机神经网络的研究和应用的兴趣重新抬头。这些“智能机器”以生物神经系统为模型，与以前作为临床决策辅助工具引入的许多计算机化专家系统有着根本的不同。作者描述了一个神经网络的设计和训练，以预测急性心肌梗死（AMI）的概率基于成对的心脏酶组的分析。与病理学家对患者实验室数据的解释相比，神经网络预测24 / 24 (100%)ami和29 / 27 (93%)no - ami （P < 0.000001）。作者试图通过两种独立的方法来验证网络的诊断。与超声心动图和心电图诊断AMI相比，神经网络在14例AMI患者中有12例（86%）和3例无AMI患者中有1例（33%）与心内科医生的解释一致，但相关性无统计学意义。使用尸检结果进行验证，神经网络与26例ami患者中的24例（92%）和6例no - ami患者中的4例（67%）的解剖证据一致（P = 0.001）。作者得出结论，神经网络可以成功地应用于心脏酶数据的分析，并表明在临床决策支持领域存在更广泛的应用。
There has been a recent resurgence of interest in the study and application of computerized neural networks within the broad field of artificial intelligence. These "intelligent machines" are modeled after biological nervous systems and are fundamentally different from the many computerized expert systems that previously have been introduced as clinical decision-making aids. The authors describe a neural network designed and trained to predict the probability of acute myocardial infarction (AMI) based on the analysis of paired sets of cardiac enzymes. The neural network predicted 24 of 24 (100%) AMIs and 27 of 29 (93%) No-AMIs when compared with a pathologist's interpretation of the patient's laboratory data (P < 0.000001). The authors attempted to validate the network's diagnoses by two independent methods. When compared with echocardiogram and EKG for diagnosis of AMI, the neural network agreed with the cardiologist's interpretation in 12 of 14 (86%) AMIs and 1 of 3 (33%) No-AMIs, but the correlation was not statistically significant. Using autopsy outcome for validation, the neural network agreed with the anatomic evidence in 24 of 26 (92%) AMIs and 4 of 6 (67%) No-AMIs (P = 0.001). The authors conclude that neural networks can be successfully applied to the analysis of cardiac enzyme data and suggest that broader applications exist within the domain of clinical decision support.