{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T23:28:28Z","timestamp":1775863708781,"version":"3.50.1"},"reference-count":69,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T00:00:00Z","timestamp":1585785600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"DOI":"10.1371\/journal.pone.0230876","type":"journal-article","created":{"date-parts":[[2020,4,2]],"date-time":"2020-04-02T17:38:58Z","timestamp":1585849138000},"page":"e0230876","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":41,"title":["Risk of mortality and cardiopulmonary arrest in critical patients presenting to the emergency department using machine learning and natural language processing"],"prefix":"10.1371","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7203-2832","authenticated-orcid":true,"given":"Marta","family":"Fernandes","sequence":"first","affiliation":[]},{"given":"R\u00faben","family":"Mendes","sequence":"additional","affiliation":[]},{"given":"Susana M.","family":"Vieira","sequence":"additional","affiliation":[]},{"given":"Francisca","family":"Leite","sequence":"additional","affiliation":[]},{"given":"Carlos","family":"Palos","sequence":"additional","affiliation":[]},{"given":"Alistair","family":"Johnson","sequence":"additional","affiliation":[]},{"given":"Stan","family":"Finkelstein","sequence":"additional","affiliation":[]},{"given":"Steven","family":"Horng","sequence":"additional","affiliation":[]},{"given":"Leo Anthony","family":"Celi","sequence":"additional","affiliation":[]}],"member":"340","published-online":{"date-parts":[[2020,4,2]]},"reference":[{"key":"pone.0230876.ref001","volume-title":"Emergency triage: Manchester triage group","author":"Kevin Mackway-Jones","year":"2014"},{"issue":"7","key":"pone.0230876.ref002","doi-asserted-by":"crossref","first-page":"653","DOI":"10.1136\/adc.2010.206797","article-title":"Undertriage in the Manchester triage system: an assessment of severity and options for improvement","volume":"96","author":"N Seiger","year":"2011","journal-title":"Archives of disease in childhood"},{"issue":"7","key":"pone.0230876.ref003","doi-asserted-by":"crossref","first-page":"1532","DOI":"10.1111\/jan.12304","article-title":"Triage: an investigation of the process and potential vulnerabilities","volume":"70","author":"M Hitchcock","year":"2014","journal-title":"Journal of advanced nursing"},{"issue":"5","key":"pone.0230876.ref004","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1016\/j.jen.2011.01.016","article-title":"Under-triage as a significant factor affecting transfer time between the emergency department and the intensive care unit","volume":"37","author":"I Yurkova","year":"2011","journal-title":"Journal of Emergency Nursing"},{"issue":"6","key":"pone.0230876.ref005","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.jamcollsurg.2010.08.014","article-title":"Survival of the fittest: the hidden cost of undertriage of major trauma","volume":"211","author":"B Haas","year":"2010","journal-title":"Journal of the American College of Surgeons"},{"issue":"1","key":"pone.0230876.ref006","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s12245-017-0161-8","article-title":"Accuracy of emergency department triage using the emergency severity index and independent predictors of under-triage and over-triage in Brazil: a retrospective cohort analysis","volume":"11","author":"JS Hinson","year":"2018","journal-title":"International journal of emergency medicine"},{"key":"pone.0230876.ref007","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1007\/978-3-319-43742-2_3","volume-title":"Secondary Analysis of Electronic Health Records","author":"S Nair","year":"2016"},{"issue":"1","key":"pone.0230876.ref008","doi-asserted-by":"crossref","first-page":"416","DOI":"10.1186\/2193-1801-2-416","article-title":"Comparison of adaptive neuro-fuzzy inference system and artificial neutral networks model to categorize patients in the emergency department","volume":"2","author":"D Azeez","year":"2013","journal-title":"SpringerPlus"},{"issue":"5","key":"pone.0230876.ref009","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.annemergmed.2017.08.005","article-title":"Machine-learning-based electronic triage more accurately differentiates patients with respect to clinical outcomes compared with the emergency severity index","volume":"71","author":"S Levin","year":"2018","journal-title":"Annals of emergency medicine"},{"issue":"9","key":"pone.0230876.ref010","doi-asserted-by":"crossref","first-page":"11078","DOI":"10.1016\/j.eswa.2011.02.152","article-title":"Analysis by data mining in the emergency medicine triage database at a Taiwanese regional hospital","volume":"38","author":"WT Lin","year":"2011","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"pone.0230876.ref011","doi-asserted-by":"crossref","first-page":"378","DOI":"10.1111\/j.1365-2753.2010.01592.x","article-title":"Classification of patients by severity grades during triage in the emergency department using data mining methods","volume":"18","author":"D Zmiri","year":"2012","journal-title":"Journal of evaluation in clinical practice"},{"key":"pone.0230876.ref012","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1007\/978-3-319-19387-8_361","volume-title":"World Congress on Medical Physics and Biomedical Engineering, June 7-12, 2015, Toronto, Canada","author":"ES Velarde","year":"2015"},{"key":"pone.0230876.ref013","doi-asserted-by":"crossref","unstructured":"Aziz D, Ali MM, Gan KB, Saiboon I. Initialization of adaptive neuro-fuzzy inference system using fuzzy clustering in predicting primary triage category. In 2012 4th International Conference on Intelligent and Advanced Systems (ICIAS2012) 2012 Jun 12 (Vol. 1, pp. 170-174). IEEE.","DOI":"10.1109\/ICIAS.2012.6306181"},{"issue":"4","key":"pone.0230876.ref014","doi-asserted-by":"crossref","first-page":"419","DOI":"10.3233\/THC-150907","article-title":"Secondary triage classification using an ensemble random forest technique","volume":"23","author":"D Azeez","year":"2015","journal-title":"Technology and Health Care"},{"key":"pone.0230876.ref015","doi-asserted-by":"crossref","first-page":"415","DOI":"10.1007\/978-3-642-36527-0_27","volume-title":"Fuzziness and Medicine: Philosophical Reflections and Application Systems in Health Care","author":"VC Georgopoulos","year":"2013"},{"issue":"5","key":"pone.0230876.ref016","doi-asserted-by":"crossref","first-page":"9968","DOI":"10.1007\/s10916-013-9968-x","article-title":"Construct an optimal triage prediction model: A case study of the emergency department of a teaching hospital in Taiwan","volume":"37","author":"ST Wang","year":"2013","journal-title":"Journal of medical systems"},{"issue":"4","key":"pone.0230876.ref017","doi-asserted-by":"crossref","first-page":"2733","DOI":"10.1016\/j.eswa.2009.08.006","article-title":"Abnormal diagnosis of Emergency Department triage explored with data mining technology: An Emergency Department at a Medical Center in Taiwan taken as an example","volume":"37","author":"WT Lin","year":"2010","journal-title":"Expert Systems with Applications"},{"issue":"4","key":"pone.0230876.ref018","doi-asserted-by":"crossref","first-page":"1305","DOI":"10.1016\/j.eswa.2012.08.060","article-title":"Rank aggregation methods comparison: A case for triage prioritization","volume":"40","author":"EB Fields","year":"2013","journal-title":"Expert Systems with Applications"},{"key":"pone.0230876.ref019","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1007\/978-3-319-11457-6_18","volume-title":"Simulation and Modeling Methodologies, Technologies and Applications","author":"VC Georgopoulos","year":"2015"},{"key":"pone.0230876.ref020","doi-asserted-by":"crossref","unstructured":"Georgopoulos VC, Stylios CD. Fuzzy cognitive maps for decision making in triage of non-critical elderly patients. In 2017 International Conference on Intelligent Informatics and Biomedical Sciences (ICIIBMS) 2017 Nov 24 (pp. 225-228). IEEE.","DOI":"10.1109\/ICIIBMS.2017.8279752"},{"issue":"18","key":"pone.0230876.ref021","doi-asserted-by":"crossref","first-page":"484","DOI":"10.3182\/20120829-3-HU-2029.00107","article-title":"Introducing fuzzy cognitive maps for developing decision support system for triage at emergency room admissions for the elderly","volume":"45","author":"VC Georgopoulos","year":"2012","journal-title":"IFAC Proceedings Volumes"},{"key":"pone.0230876.ref022","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1016\/j.ijmedinf.2018.03.008","article-title":"Decision support system for triage management: A hybrid approach using rule-based reasoning and fuzzy logic","volume":"114","author":"MD Soufi","year":"2018","journal-title":"International journal of medical informatics"},{"key":"pone.0230876.ref023","unstructured":"Lin WT, Jou YT, Wu YC, Hsiao YD. Data Mining Applied to the Predictive Model of Triage System in Emergency Department. In Proceedings of World Academy of Science, Engineering and Technology 2013 Jan 1 (No. 78, p. 1789). World Academy of Science, Engineering and Technology (WASET)."},{"issue":"4","key":"pone.0230876.ref024","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1111\/1742-6723.12425","article-title":"Model to predict inpatient mortality from information gathered at presentation to an emergency department: The Triage Information Mortality Model (TIMM)","volume":"27","author":"DJ Teubner","year":"2015","journal-title":"Emergency Medicine Australasia"},{"issue":"6","key":"pone.0230876.ref025","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1007\/s00134-015-3737-x","article-title":"A clinical prediction model to identify patients at high risk of death in the emergency department","volume":"41","author":"M Coslovsky","year":"2015","journal-title":"Intensive care medicine"},{"issue":"4","key":"pone.0230876.ref026","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1097\/MEJ.0b013e328344917e","article-title":"Increasing wait times predict increasing mortality for emergency medical admissions","volume":"18","author":"PK Plunkett","year":"2011","journal-title":"European Journal of Emergency Medicine"},{"issue":"1","key":"pone.0230876.ref027","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1186\/s13054-019-2351-7","article-title":"Emergency department triage prediction of clinical outcomes using machine learning models","volume":"23","author":"Y Raita","year":"2019","journal-title":"Critical Care"},{"issue":"5","key":"pone.0230876.ref028","doi-asserted-by":"crossref","first-page":"453","DOI":"10.5811\/westjem.2013.5.13411","article-title":"Predictive value of initial triage vital signs for critically ill older adults","volume":"14","author":"MA LaMantia","year":"2013","journal-title":"Western Journal of Emergency Medicine"},{"issue":"1","key":"pone.0230876.ref029","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1186\/1757-7241-20-28","article-title":"Abnormal vital signs are strong predictors for intensive care unit admission and in-hospital mortality in adults triaged in the emergency department-a prospective cohort study","volume":"20","author":"C Barfod","year":"2012","journal-title":"Scandinavian journal of trauma, resuscitation and emergency medicine"},{"key":"pone.0230876.ref030","doi-asserted-by":"crossref","DOI":"10.1155\/2018\/7174803","article-title":"An evolutionary computation approach for optimizing multilevel data to predict patient outcomes","volume":"2018","author":"S Barnes","year":"2018","journal-title":"Journal of healthcare engineering"},{"issue":"10","key":"pone.0230876.ref031","doi-asserted-by":"crossref","first-page":"1155","DOI":"10.1111\/acem.12755","article-title":"Are mortality and acute morbidity in patients presenting with nonspecific complaints predictable using routine variables?","volume":"22","author":"MA Jenny","year":"2015","journal-title":"Academic Emergency Medicine"},{"issue":"11","key":"pone.0230876.ref032","doi-asserted-by":"crossref","first-page":"767","DOI":"10.7326\/0003-4819-156-11-201206050-00003","article-title":"Prediction of heart failure mortality in emergent care: a cohort study","volume":"156","author":"DS Lee","year":"2012","journal-title":"Annals of internal medicine"},{"issue":"3","key":"pone.0230876.ref033","doi-asserted-by":"crossref","first-page":"R108","DOI":"10.1186\/cc11396","article-title":"Prediction of cardiac arrest in critically ill patients presenting to the emergency department using a machine learning score incorporating heart rate variability compared with the modified early warning score","volume":"16","author":"ME Ong","year":"2012","journal-title":"Critical Care"},{"issue":"1","key":"pone.0230876.ref034","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1097\/MEJ.0b013e32834fdcf3","article-title":"How accurate are vital signs in predicting clinical outcomes in critically ill emergency department patients","volume":"20","author":"W Hong","year":"2013","journal-title":"European Journal of Emergency Medicine"},{"issue":"1","key":"pone.0230876.ref035","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1186\/s13049-016-0213-8","article-title":"The association between vital signs and mortality in a retrospective cohort study of an unselected emergency department population","volume":"24","author":"M Ljunggren","year":"2016","journal-title":"Scandinavian journal of trauma, resuscitation and emergency medicine"},{"issue":"4","key":"pone.0230876.ref036","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1097\/MEJ.0000000000000164","article-title":"Decreased general condition in the emergency department: high in-hospital mortality and a broad range of discharge diagnoses","volume":"22","author":"T Dj\u00e4rv","year":"2015","journal-title":"European Journal of Emergency Medicine"},{"issue":"4","key":"pone.0230876.ref037","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1111\/1742-6723.12426","article-title":"Triage-based resource allocation and clinical treatment protocol on outcome and length of stay in the emergency department","volume":"27","author":"YS Ro","year":"2015","journal-title":"Emergency Medicine Australasia"},{"issue":"3","key":"pone.0230876.ref038","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1111\/j.1553-2712.2009.00675.x","article-title":"Predicting hospital admission and returns to the emergency department for elderly patients","volume":"17","author":"MA LaMantia","year":"2010","journal-title":"Academic emergency medicine"},{"issue":"5","key":"pone.0230876.ref039","first-page":"224","article-title":"Building a decision support system for inpatient admission prediction with the Manchester triage system and administrative check-in variables","volume":"34","author":"A Zlotnik","year":"2016","journal-title":"CIN: Computers, Informatics, Nursing"},{"issue":"3","key":"pone.0230876.ref040","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1136\/emermed-2013-203200","article-title":"A simple tool to predict admission at the time of triage","volume":"32","author":"A Cameron","year":"2015","journal-title":"Emergency Medicine Journal"},{"key":"pone.0230876.ref041","doi-asserted-by":"crossref","first-page":"10458","DOI":"10.1109\/ACCESS.2018.2808843","article-title":"Using data mining to predict hospital admissions from the emergency department","volume":"6","author":"B Graham","year":"2018","journal-title":"IEEE Access"},{"issue":"05","key":"pone.0230876.ref042","doi-asserted-by":"crossref","first-page":"377","DOI":"10.3414\/ME17-01-0024","article-title":"Prediction of emergency department hospital admission based on natural language processing and neural networks","volume":"56","author":"X Zhang","year":"2017","journal-title":"Methods of information in medicine"},{"issue":"2","key":"pone.0230876.ref043","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1097\/MEJ.0000000000000126","article-title":"Evaluation of a hospital admission prediction model adding coded chief complaint data using neural network methodology","volume":"22","author":"N Handly","year":"2015","journal-title":"European Journal of Emergency Medicine"},{"issue":"7","key":"pone.0230876.ref044","doi-asserted-by":"crossref","first-page":"794","DOI":"10.1016\/j.ajem.2007.01.014","article-title":"Identifying high-risk patients for triage and resource allocation in the ED","volume":"25","author":"JP Ruger","year":"2007","journal-title":"The American journal of emergency medicine"},{"issue":"5","key":"pone.0230876.ref045","doi-asserted-by":"crossref","first-page":"762","DOI":"10.1017\/cem.2018.17","article-title":"Characteristics and outcomes of older emergency department patients assigned a low acuity triage score","volume":"20","author":"A Hendin","year":"2018","journal-title":"Canadian Journal of Emergency Medicine"},{"issue":"8","key":"pone.0230876.ref046","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1111\/j.1553-2712.2011.01125.x","article-title":"Predicting hospital admissions at emergency department triage using routine administrative data","volume":"18","author":"Y Sun","year":"2011","journal-title":"Academic Emergency Medicine"},{"key":"pone.0230876.ref047","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.ijpe.2018.11.024","article-title":"Predictive analytics for hospital admissions from the emergency department using triage information","volume":"208","author":"OM Araz","year":"2019","journal-title":"International Journal of Production Economics"},{"issue":"7","key":"pone.0230876.ref048","article-title":"Predicting hospital admission at emergency department triage using machine learning","volume":"13","author":"WS Hong","year":"2018","journal-title":"PloS one"},{"key":"pone.0230876.ref049","article-title":"Predicting hospital admission at the emergency department triage: A novel prediction model","volume":"2018","author":"CA Parker","year":"2018","journal-title":"The American journal of emergency medicine"},{"issue":"26","key":"pone.0230876.ref050","article-title":"Predictive factors for hospitalization of nonurgent patients in the emergency department","volume":"95","author":"CJ Ng","year":"2016","journal-title":"Medicine"},{"issue":"4","key":"pone.0230876.ref051","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1111\/1742-6723.12252","article-title":"Predicting admission of patients by their presentation to the emergency department","volume":"26","author":"SW Kim","year":"2014","journal-title":"Emergency Medicine Australasia"},{"issue":"4","key":"pone.0230876.ref052","doi-asserted-by":"crossref","first-page":"S138","DOI":"10.1016\/j.annemergmed.2013.07.212","article-title":"Derivation and validation of a hospital admission prediction model adding coded chief complaint to demographic, emergency department operational and patient acuity data available at emergency department triage using neural net methodology","volume":"62","author":"N Handly","year":"2013","journal-title":"Annals of Emergency Medicine"},{"issue":"5","key":"pone.0230876.ref053","doi-asserted-by":"crossref","first-page":"843","DOI":"10.1111\/1742-6723.13267","article-title":"Predictors of in-hospital cardiac arrest within 24 h after emergency department triage: A case\u2013control study in urban Thailand","volume":"31","author":"W Srivilaithon","year":"2019","journal-title":"Emergency Medicine Australasia"},{"issue":"4","key":"pone.0230876.ref054","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0123803","article-title":"Unscheduled-return-visits after an emergency department (ED) attendance and clinical link between both visits in patients aged 75 years and over: a prospective observational study","volume":"10","author":"L Pereira","year":"2015","journal-title":"PloS one"},{"key":"pone.0230876.ref055","doi-asserted-by":"crossref","unstructured":"Gligorijevic D, et al. Deep attention model for triage of emergency department patients. In Proceedings of the 2018 SIAM International Conference on Data Mining 2018 May 7 (pp. 297-305). Society for Industrial and Applied Mathematics.","DOI":"10.1137\/1.9781611975321.34"},{"key":"pone.0230876.ref056","first-page":"101762","article-title":"Clinical Decision Support Systems for Triage in the Emergency Department using Intelligent Systems: a Review","author":"M Fernandes","year":"2019","journal-title":"Artificial Intelligence in Medicine"},{"key":"pone.0230876.ref057","volume-title":"Emergency Care Services: Trends, Drivers and Interventions to Manage the Demand","author":"C Berchet","year":"2015"},{"key":"pone.0230876.ref058","doi-asserted-by":"crossref","unstructured":"Cosgriff, Christopher V., et al. Developing well-calibrated illness severity scores for decision support in the critically ill. npj Digital Medicine 2.1 (2019): 1-8.","DOI":"10.1038\/s41746-019-0153-6"},{"key":"pone.0230876.ref059","doi-asserted-by":"crossref","unstructured":"Pappachan, John V., et al. Comparison of outcome from intensive care admission after adjustment for case mix by the APACHE III prognostic system. Chest 115.3 (1999): 802-810.","DOI":"10.1378\/chest.115.3.802"},{"issue":"9","key":"pone.0230876.ref060","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.1097\/00003246-199409000-00007","article-title":"Intensive Care Society\u2019s Acute Physiology and Chronic Health Evaluation (APACHE II) study in Britain and Ireland: a prospective, multicenter, cohort study comparing two methods for predicting outcome for adult intensive care patients","volume":"22","author":"Kathryn M. Rowan","year":"1994","journal-title":"Critical care medicine"},{"issue":"6910","key":"pone.0230876.ref061","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1136\/bmj.307.6910.977","article-title":"Intensive Care Society\u2019s APACHE II study in Britain and Ireland\u2013II: Outcome comparisons of intensive care units after adjustment for case mix by the American APACHE II method","volume":"307","author":"K. M. Rowan","year":"1993","journal-title":"Bmj"},{"issue":"13","key":"pone.0230876.ref062","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1001\/jama.2017.18391","article-title":"Big data and machine learning in health care","volume":"319","author":"Andrew L. Beam","year":"2018","journal-title":"Jama"},{"issue":"1","key":"pone.0230876.ref063","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"L Breiman","year":"2001","journal-title":"Machine learning"},{"key":"pone.0230876.ref064","first-page":"1189","article-title":"Greedy function approximation: a gradient boosting machine Annals of statistics","author":"Jerome H. Friedman","year":"2001","journal-title":"JSTOR"},{"key":"pone.0230876.ref065","doi-asserted-by":"crossref","unstructured":"Chen, Tianqi, and Carlos Guestrin. Xgboost: A scalable tree boosting system. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. ACM, 2016.","DOI":"10.1145\/2939672.2939785"},{"key":"pone.0230876.ref066","doi-asserted-by":"crossref","unstructured":"Azari A, Janeja VP, Levin S. Imbalanced learning to predict long stay Emergency Department patients. In: Bioinformatics and Biomedicine (BIBM), 2015 IEEE International Conference on. IEEE; 2015. p. 807\u2013814.","DOI":"10.1109\/BIBM.2015.7359790"},{"issue":"1","key":"pone.0230876.ref067","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1177\/001316446002000104","article-title":"A coefficient of agreement for nominal scales","volume":"20","author":"J Cohen","year":"1960","journal-title":"Educational and psychological measurement"},{"issue":"3","key":"pone.0230876.ref068","doi-asserted-by":"crossref","first-page":"276","DOI":"10.11613\/BM.2012.031","article-title":"Interrater reliability: the kappa statistic","volume":"22","author":"ML McHugh","year":"2012","journal-title":"Biochemia medica: Biochemia medica"},{"key":"pone.0230876.ref069","unstructured":"Brownlee J. Imbalanced Classification with Python: Better Metrics, Balance Skewed Classes, Cost-Sensitive Learning. Machine Learning Mastery 2020. Accessed 5\/2\/2020. url=https:\/\/books.google.pt\/books?id=jaXJDwAAQBAJ."}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0230876","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,3,7]],"date-time":"2021-03-07T04:26:18Z","timestamp":1615091178000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0230876"}},"subtitle":[],"editor":[{"given":"Ivan","family":"Olier","sequence":"first","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2020,4,2]]},"references-count":69,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,4,2]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0230876","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,2]]}}}