{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:15:12Z","timestamp":1750220112404,"version":"3.41.0"},"reference-count":49,"publisher":"Publicidad Permanyer, SLU","issue":"3","license":[{"start":{"date-parts":[[2018,7,1]],"date-time":"2018-07-01T00:00:00Z","timestamp":1530403200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"}],"content-domain":{"domain":["elsevier.es","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Archivos de Cardiolog\u00eda de M\u00e9xico"],"published-print":{"date-parts":[[2018,7]]},"DOI":"10.1016\/j.acmx.2017.03.002","type":"journal-article","created":{"date-parts":[[2017,3,28]],"date-time":"2017-03-28T01:04:35Z","timestamp":1490663075000},"page":"178-189","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"title":["Herramientas de sistemas inteligentes en el diagn\u00f3stico de los s\u00edndromes coronarios agudos: una revisi\u00f3n sistem\u00e1tica"],"prefix":"10.24875","volume":"88","author":[{"given":"John","family":"Sprockel","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Miguel","family":"Tejeda","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jos\u00e9","family":"Yate","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Juan","family":"Diaztagle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Enrique","family":"Gonz\u00e1lez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"10499","reference":[{"key":"10.1016\/j.acmx.2017.03.002_bib0245","doi-asserted-by":"crossref","first-page":"2999","DOI":"10.1093\/eurheartj\/ehr236","volume":"32","author":"Hamm","year":"2011","journal-title":"Eur Heart J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0250","first-page":"1","article-title":"Enabling health care decisionmaking through clinical decision support and knowledge management","author":"Lobach","year":"2012","journal-title":"Evid Rep Technol Assess"},{"key":"10.1016\/j.acmx.2017.03.002_bib0255","series-title":"Artificial intelligence. A guide to intelligent systems","first-page":"440","author":"Negnevitsky","year":"2004"},{"key":"10.1016\/j.acmx.2017.03.002_bib0260","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1136\/emj.2010.092619","article-title":"Clinical value of diagnostic instruments for ruling out acute coronary syndrome in patients with chest pain: A systematic review","volume":"27","author":"Steurer","year":"2010","journal-title":"Emerg Med J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0265","doi-asserted-by":"crossref","first-page":"e000151","DOI":"10.1136\/openhrt-2014-000151","article-title":"Implementation of NICE Clinical Guideline 95 for assessment of stable chest pain in a rapid access chest pain clinic reduces the mean number of investigations and cost per patient","volume":"2","author":"Lee","year":"2015","journal-title":"Open Heart."},{"key":"10.1016\/j.acmx.2017.03.002_bib0270","doi-asserted-by":"crossref","first-page":"974","DOI":"10.1136\/hrt.2009.190066","article-title":"NICE guidance. Chest pain of recent onset: Assessment and diagnosis of recent onset chest pain or discomfort of suspected cardiac origin","volume":"96","author":"Skinner","year":"2010","journal-title":"Heart."},{"key":"10.1016\/j.acmx.2017.03.002_bib0275","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.1161\/CIR.0b013e3181ec61df","article-title":"Testing of low-risk patients presenting to the emergency department with chest pain: A scientific statement from the American Heart Association","volume":"122","author":"Amsterdam","year":"2010","journal-title":"Circulation."},{"key":"10.1016\/j.acmx.2017.03.002_bib0280","doi-asserted-by":"crossref","first-page":"529","DOI":"10.7326\/0003-4819-155-8-201110180-00009","article-title":"QUADAS-2: A revised tool for the quality assessment of diagnostic accuracy studies","volume":"155","author":"Whiting","year":"2011","journal-title":"Ann Intern Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0285","doi-asserted-by":"crossref","first-page":"843","DOI":"10.7326\/0003-4819-115-11-843","article-title":"Use of an artificial neural network for the diagnosis of myocardial infarction","volume":"115","author":"Baxt","year":"1991","journal-title":"Ann Intern Med."},{"doi-asserted-by":"crossref","unstructured":"Harrison RF, Marshall SJ, Kennedy RL. The early diagnosis of heart attacks: A neurocomputational approach. Seattle: IEEE; 1991. p. 1\u20135.","key":"10.1016\/j.acmx.2017.03.002_bib0290","DOI":"10.1109\/IJCNN.1991.155140"},{"key":"10.1016\/j.acmx.2017.03.002_bib0295","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1093\/ajcp\/96.1.134","article-title":"Neural network analysis of serial cardiac enzyme data. A clinical application of artificial machine intelligence","volume":"96","author":"Furlong","year":"1991","journal-title":"Am J Clin Pathol."},{"key":"10.1016\/j.acmx.2017.03.002_bib0300","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1093\/eurheartj\/14.4.433","article-title":"Decision support by computer analysis of selected case history variables in the emergency room among patients with acute chest pain","volume":"14","author":"Aase","year":"1993","journal-title":"Eur Heart J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0305","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/S0140-6736(96)91555-X","article-title":"Prospective validation of artificial neural network trained to identify acute myocardial infarction","volume":"347","author":"Baxt","year":"1996","journal-title":"Lancet."},{"key":"10.1016\/j.acmx.2017.03.002_bib0310","first-page":"241","article-title":"A hybrid neuro-fuzzy system for ECG classification of myocardial infarction","author":"Bozzola","year":"1996","journal-title":"Comput Cardiol."},{"key":"10.1016\/j.acmx.2017.03.002_bib0315","doi-asserted-by":"crossref","first-page":"604","DOI":"10.1093\/clinchem\/42.4.604","article-title":"Use of neural networks to diagnose acute myocardial infarction. I. Methodology","volume":"42","author":"J\u00f8rgensen","year":"1996","journal-title":"Clin Chem."},{"key":"10.1016\/j.acmx.2017.03.002_bib0320","doi-asserted-by":"crossref","first-page":"613","DOI":"10.1093\/clinchem\/42.4.613","article-title":"Use of neural networks to diagnose acute myocardial infarction. II. A clinical application","volume":"42","author":"Pedersen","year":"1996","journal-title":"Clin Chem."},{"key":"10.1016\/j.acmx.2017.03.002_bib0325","doi-asserted-by":"crossref","first-page":"689","DOI":"10.1111\/j.1553-2712.1996.tb03492.x","article-title":"Criteria-based expert system for cardiac ischemia evaluation in the emergency department","volume":"3","author":"Hirshberg","year":"1996","journal-title":"Acad Emerg Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0330","doi-asserted-by":"crossref","first-page":"1919","DOI":"10.1093\/clinchem\/43.10.1919","article-title":"Early assessment of patients with suspected acute myocardial infarction by biochemical monitoring and neural network analysis","volume":"43","author":"Ellenius","year":"1997","journal-title":"Clin Chem."},{"key":"10.1016\/j.acmx.2017.03.002_bib0335","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/S0169-2607(96)01782-8","article-title":"An artificial neural network system for diagnosis of acute myocardial infarction (AMI) in the accident and emergency department: Evaluation and comparison with serum myoglobin measurements","volume":"52","author":"Kennedy","year":"1997","journal-title":"Comput Methods Programs Biomed."},{"key":"10.1016\/j.acmx.2017.03.002_bib0340","doi-asserted-by":"crossref","first-page":"1798","DOI":"10.1161\/01.CIR.96.6.1798","article-title":"Acute myocardial infarction detected in the 12-lead ECG by artificial neural networks","volume":"96","author":"Hed\u00e9n","year":"1997","journal-title":"Circulation."},{"key":"10.1016\/j.acmx.2017.03.002_bib0345","first-page":"493","article-title":"Using classification tree and logistic regression methods to diagnose myocardial infarction","volume":"52 Pt 1","author":"Tsien","year":"1998","journal-title":"Stud Health Technol Inform."},{"key":"10.1016\/j.acmx.2017.03.002_bib0350","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1159\/000006960","article-title":"Clinical experience with a decision support computer program using Bayes\u2019 theorem to diagnose chest pain patients","volume":"92","author":"Aase","year":"1999","journal-title":"Cardiology."},{"key":"10.1016\/j.acmx.2017.03.002_bib0355","first-page":"246","article-title":"Evaluating variable selection methods for diagnosis of myocardial infarction","author":"Dreiseitl","year":"1999","journal-title":"Proc AMIA Symp."},{"doi-asserted-by":"crossref","unstructured":"Lu HL, Ong K, Chia P. Automated ECG classification system based on a neuro-fuzzy system. Comput Cardiol. 2000;27:387\u201390.","key":"10.1016\/j.acmx.2017.03.002_bib0360","DOI":"10.1109\/CIC.2000.898538"},{"key":"10.1016\/j.acmx.2017.03.002_bib0365","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S0010-4825(00)00022-6","article-title":"Using patient-reportable clinical history factors to predict myocardial infarction","volume":"31","author":"Wang","year":"2001","journal-title":"Comput Biol Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0370","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1067\/mem.2002.122705","article-title":"A neural computational aid to the diagnosis of acute myocardial infarction","volume":"39","author":"Baxt","year":"2002","journal-title":"Ann Emerg Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0375","doi-asserted-by":"crossref","first-page":"575","DOI":"10.1067\/mem.2002.129171","article-title":"A neural network aid for the early diagnosis of cardiac ischemia in patients presenting to the emergency department with chest pain","volume":"40","author":"Baxt","year":"2002","journal-title":"Ann Emerg Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0380","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1046\/j.1475-097X.2002.00433.x","article-title":"Neural networks\u2013a diagnostic tool in acute myocardial infarction with concomitant left bundle branch block","volume":"22","author":"Olsson","year":"2002","journal-title":"Clin Physiol Funct Imaging."},{"key":"10.1016\/j.acmx.2017.03.002_bib0385","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2004.01.003","article-title":"Detecting acute myocardial infarction in the 12-lead ECG using Hermite expansions and neural networks","volume":"32","author":"Haraldsson","year":"2004","journal-title":"Artif Intell Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0390","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1016\/j.annemergmed.2004.09.012","article-title":"Artificial neural network models for prediction of acute coronary syndromes using clinical data from the time of presentation","volume":"46","author":"Harrison","year":"2005","journal-title":"Ann Emerg Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0395","doi-asserted-by":"crossref","first-page":"401","DOI":"10.1080\/10556780512331318164","article-title":"Kernel-based support vector machine classifiers for early detection of myocardial infarction","volume":"20","author":"Conforti","year":"2005","journal-title":"Optim Methods Softw."},{"key":"10.1016\/j.acmx.2017.03.002_bib0400","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1177\/1460458206066665","article-title":"Using neural networks and just nine patient-reportable factors of screen for AMI","volume":"12","author":"Bulgiba","year":"2006","journal-title":"Health Informatics J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0405","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1536\/ihj.47.491","article-title":"A computer based telemedicine protocol to predict acute coronary syndrome in patients with chest pain at home","volume":"47","author":"Coskun","year":"2006","journal-title":"Int Heart J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0410","doi-asserted-by":"crossref","first-page":"151","DOI":"10.1111\/j.1475-097X.2006.00669.x","article-title":"Decision support for the initial triage of patients with acute coronary syndromes","volume":"26","author":"Olsson","year":"2006","journal-title":"Clin Physiol Funct Imaging."},{"key":"10.1016\/j.acmx.2017.03.002_bib0415","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.artmed.2006.07.006","article-title":"Comparison between neural networks and multiple logistic regression to predict acute coronary syndrome in the emergency room","volume":"38","author":"Green","year":"2006","journal-title":"Artif Intell Med."},{"doi-asserted-by":"crossref","unstructured":"Al-Naima FM, Ali AH, Mahdi SS. Data acquisition for myocardial infarction classification based on wavelets and Neural Networks. In IEEE SSD 2008. 5th International Multi-Conference on Systems, Signals and Devices; 2008. p. 1\u20136.","key":"10.1016\/j.acmx.2017.03.002_bib0420","DOI":"10.1109\/SSD.2008.4632817"},{"doi-asserted-by":"crossref","unstructured":"Al-Naima FM, Ali AH, Mahdi SS. Data acquisition for myocardial infarction classification based on wavelets and Neural Networks. Seattle: IEEE; 2008. p. 1\u20136.","key":"10.1016\/j.acmx.2017.03.002_bib0425","DOI":"10.1109\/SSD.2008.4632817"},{"key":"10.1016\/j.acmx.2017.03.002_bib0430","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.jelectrocard.2008.07.010","article-title":"In search of the best method to predict acute coronary syndrome using only the electrocardiogram from the emergency department","volume":"42","author":"Forberg","year":"2009","journal-title":"J Electrocardiol."},{"key":"10.1016\/j.acmx.2017.03.002_bib0435","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1007\/s10916-010-9491-2","article-title":"AMI screening using linguistic fuzzy rules","volume":"36","author":"Ainon","year":"2010","journal-title":"J Med Systems."},{"key":"10.1016\/j.acmx.2017.03.002_bib0440","doi-asserted-by":"crossref","first-page":"3165","DOI":"10.1016\/j.asoc.2012.06.004","article-title":"Myocardial infarction classification with multi-lead ECG using hidden Markov models and Gaussian mixture models","volume":"12","author":"Chang","year":"2012","journal-title":"Appl Soft Comput."},{"key":"10.1016\/j.acmx.2017.03.002_bib0445","first-page":"2704","article-title":"Detection of acute myocardial infarction from serial ECG using multilayer support vector machine","volume":"2012","author":"Dhawan","year":"2012","journal-title":"Conf Proc IEEE Eng Med Biol Soc."},{"key":"10.1016\/j.acmx.2017.03.002_bib0450","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1186\/1742-4682-10-57","article-title":"Application of pattern recognition tools for classifying acute coronary syndrome: An integrated medical modeling","volume":"10","author":"Salari","year":"2013","journal-title":"Theor Biol Med Model."},{"key":"10.1016\/j.acmx.2017.03.002_bib0455","doi-asserted-by":"crossref","first-page":"5004","DOI":"10.1016\/j.eswa.2013.03.029","article-title":"Expert system for predicting unstable angina based on Bayesian networks","volume":"40","author":"Vila-Franc\u00e9s","year":"2013","journal-title":"Expert Syst Appl."},{"doi-asserted-by":"crossref","unstructured":"Pozen MW, D\u2019Agostino RB, Mitchell JB, et al. The usefulness of a predictive instrument to reduce inappropriate admissions to the coronary care unit. Ann Intern Med. 1980; 92 (2 Pt 1): 238\u201342.","key":"10.1016\/j.acmx.2017.03.002_bib0460","DOI":"10.7326\/0003-4819-92-2-238"},{"doi-asserted-by":"crossref","unstructured":"Hess EP, Thiruganasambandamoorthy V, Wells GA, et al. Diagnostic accuracy of clinical prediction rules to exclude acute coronary syndrome in the emergency department setting: a systematic review. CJEM. 2008; 10: 373\u201382.","key":"10.1016\/j.acmx.2017.03.002_bib0470","DOI":"10.1017\/S148180350001040X"},{"key":"10.1016\/j.acmx.2017.03.002_bib0465","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.jclinepi.2006.06.002","article-title":"We should overcome the barriers to evidence-based clinical diagnosis!","volume":"60","author":"Richardson","year":"2007","journal-title":"J Clin Epidemiol."},{"key":"10.1016\/j.acmx.2017.03.002_bib0475","doi-asserted-by":"crossref","first-page":"441","DOI":"10.1093\/eurheartj\/14.4.441","article-title":"Prospective evaluation of an EDB-based diagnostic program to be used in patients admitted to hospital with acute chest pain","volume":"14","author":"Jonsbu","year":"1993","journal-title":"Eur Heart J."},{"key":"10.1016\/j.acmx.2017.03.002_bib0480","doi-asserted-by":"crossref","first-page":"1098","DOI":"10.1016\/j.ajem.2013.04.013","article-title":"An algorithm for identification of ST-elevation myocardial infarction patients by emergency medicine services","volume":"31","author":"Wilson","year":"2013","journal-title":"Am J Emerg Med."},{"key":"10.1016\/j.acmx.2017.03.002_bib0485","doi-asserted-by":"crossref","first-page":"751","DOI":"10.7326\/0003-4819-138-9-200305060-00013","article-title":"Competency in interpretation of 12-lead electrocardiograms: A summary and appraisal of published evidence","volume":"138","author":"Salerno","year":"2003","journal-title":"Ann Intern Med."}],"container-title":["Archivos de Cardiolog\u00eda de M\u00e9xico"],"original-title":[],"language":"es","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1405994017300186?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1405994017300186?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/www.archivoscardiologia.com\/previos\/(2018)%20ACM%20Vol%2088.%203%20JULIO-SEPTIEMBRE\/ACMX_2018_88_3_178-189.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T18:15:35Z","timestamp":1750184135000},"score":1,"resource":{"primary":{"URL":"http:\/\/www.archivoscardiologia.com\/previos\/(2018)%20ACM%20Vol%2088.%203%20JULIO-SEPTIEMBRE\/ACMX_2018_88_3_178-189.pdf"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":49,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2018,7]]}},"alternative-id":["S1405994017300186"],"URL":"https:\/\/doi.org\/10.1016\/j.acmx.2017.03.002","relation":{},"ISSN":["1405-9940"],"issn-type":[{"type":"print","value":"1405-9940"}],"subject":[],"published":{"date-parts":[[2018,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Herramientas de sistemas inteligentes en el diagn\u00f3stico de los s\u00edndromes coronarios agudos: una revisi\u00f3n sistem\u00e1tica","name":"articletitle","label":"Article Title"},{"value":"Archivos de Cardiolog\u00eda de M\u00e9xico","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.acmx.2017.03.002","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2017 Instituto Nacional de Cardiolog\u00eda Ignacio Ch\u00e1vez. Published by Masson Doyma M\u00e9xico S.A.","name":"copyright","label":"Copyright"}]}}