title: [Intelligent systems tools in the diagnosis of acute coronary syndromes: A systemic review].
abstract: 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.
authors.affiliation: Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia. Electronic address: jjsprockel@fucsalud.edu.co.
authors.forename: John
authors.initials: J
authors.lastname: Sprockel
authors.affiliation: Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia.
authors.forename: Miguel
authors.initials: M
authors.lastname: Tejeda
authors.affiliation: Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia.
authors.forename: José
authors.initials: J
authors.lastname: Yate
authors.affiliation: Departamento de Medicina Interna, Hospital de San José, Fundación Universitaria de Ciencias de la Salud, Bogotá, Colombia; Departamento de Ciencias Fisiologicas, Universidad Nacional de Colombia, Bogotá, Colombia.
authors.forename: Juan
authors.initials: J
authors.lastname: Diaztagle
authors.affiliation: Departamento de Ingeniería de Sistemas, Pontificia Universidad Javeriana, Bogotá, Colombia.
authors.forename: Enrique
authors.initials: E
authors.lastname: González
mesh_terms: D054058:Acute Coronary Syndrome / Q000175:diagnosis*; D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D004562:Electrocardiography; D006801:Humans; D008962:Models, Theoretical; D009203:Myocardial Infarction / Q000175:diagnosis*; D016571:Neural Networks, Computer; D060388:Support Vector Machine
keywords: Angina inestable; Artificial intelligence; Colombia; Diagnosis; Diagnóstico; Infarto de miocardio; Inteligencia artificial; Myocardial infarction; Unstable angina