Sprockel John, Tejeda Miguel, Yate José, Diaztagle Juan, González Enrique
Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia.
Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia.
Arch Cardiol Mex. 2018 Jul-Sep;88(3):178-189. doi: 10.1016/j.acmx.2017.03.002. Epub 2017 Mar 27.
Acute myocardial infarction is the leading cause of non-communicable deaths worldwide. Its diagnosis is a highly complex task, for which modelling through automated methods has been attempted. A systematic review of the literature was performed on diagnostic tests that applied intelligent systems tools in the diagnosis of acute coronary syndromes.
A systematic review of the literature is presented using Medline, Embase, Scopus, IEEE/IET Electronic Library, ISI Web of Science, Latindex and LILACS databases for articles that include the diagnostic evaluation of acute coronary syndromes using intelligent systems. The review process was conducted independently by 2 reviewers, and discrepancies were resolved through the participation of a third person. The operational characteristics of the studied tools were extracted.
A total of 35 references met the inclusion criteria. In 22 (62.8%) cases, neural networks were used. In five studies, the performances of several intelligent systems tools were compared. Thirteen studies sought to perform diagnoses of all acute coronary syndromes, and in 22, only infarctions were studied. In 21 cases, clinical and electrocardiographic aspects were used as input data, and in 10, only electrocardiographic data were used. Most intelligent systems use the clinical context as a reference standard. High rates of diagnostic accuracy were found with better performance using neural networks and support vector machines, compared with statistical tools of pattern recognition and decision trees.
Extensive evidence was found that shows that using intelligent systems tools achieves a greater degree of accuracy than some clinical algorithms or scales and, thus, should be considered appropriate tools for supporting diagnostic decisions of acute coronary syndromes.
急性心肌梗死是全球非传染性死亡的主要原因。其诊断是一项高度复杂的任务，人们已尝试通过自动化方法进行建模。对应用智能系统工具诊断急性冠状动脉综合征的诊断测试进行了文献系统综述。
使用Medline、Embase、Scopus、IEEE/IET电子图书馆、ISI科学网、Latindex和LILACS数据库对文献进行系统综述，以查找包含使用智能系统对急性冠状动脉综合征进行诊断评估的文章。综述过程由2名评审员独立进行，差异通过第三人参与解决。提取了所研究工具的操作特征。
共有35篇参考文献符合纳入标准。其中22篇（62.8%）使用了神经网络。在5项研究中，比较了几种智能系统工具的性能。13项研究试图对所有急性冠状动脉综合征进行诊断，22项研究仅研究了梗死情况。21项研究将临床和心电图方面用作输入数据，10项研究仅使用了心电图数据。大多数智能系统将临床背景作为参考标准。与模式识别统计工具和决策树相比，使用神经网络和支持向量机的诊断准确率更高，性能更好。
有大量证据表明，使用智能系统工具比某些临床算法或量表具有更高的准确性，因此应被视为支持急性冠状动脉综合征诊断决策的合适工具。
