{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T19:11:09Z","timestamp":1790881869269,"version":"4.1.0"},"reference-count":28,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,10,1]],"date-time":"2024-10-01T00:00:00Z","timestamp":1727740800000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["clinicalkey.fr","clinicalkey.jp","clinicalkey.es","clinicalkey.com.au","clinicalkey.com","internationaljournalofcardiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Cardiology"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1016\/j.ijcard.2024.132339","type":"journal-article","created":{"date-parts":[[2024,7,3]],"date-time":"2024-07-03T21:23:44Z","timestamp":1720041824000},"page":"132339","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":19,"special_numbering":"C","title":["Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm"],"prefix":"10.1016","volume":"412","author":[{"given":"Panagiotis G.","family":"Asteris","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eleni","family":"Gavriilaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Polydoros N.","family":"Kampaktsis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amir H.","family":"Gandomi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danial J.","family":"Armaghani","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Markos Z.","family":"Tsoukalas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dimitrios V.","family":"Avgerinos","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Savvas","family":"Grigoriadis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nikolaos","family":"Kotsiou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Efthalia","family":"Yannaki","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasios","family":"Drougkas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Abidhan","family":"Bardhan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liborio","family":"Cavaleri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Antonio","family":"Formisano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ahmed Salih","family":"Mohammed","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhatawdekar Ramesh","family":"Murlidhar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Satish","family":"Paudel","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pijush","family":"Samui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Panteleimon","family":"Sarafidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Agostino","family":"Virdis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eugenia","family":"Gkaliagkousi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijcard.2024.132339_bb0005","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1016\/j.ecl.2022.02.005","article-title":"Assessment of cardiovascular disease risk: a 2022 update","volume":"51","author":"Goldsborough","year":"2022","journal-title":"Endocrinol. Metab. Clin. N. Am."},{"key":"10.1016\/j.ijcard.2024.132339_bb0010","doi-asserted-by":"crossref","first-page":"3433","DOI":"10.3390\/nu14163433","article-title":"Biomarkers for non-invasive stratification of coronary artery disease and prognostic impact on long-term survival in patients with stable coronary heart disease","volume":"14","author":"Netto","year":"2022","journal-title":"Nutrients"},{"key":"10.1016\/j.ijcard.2024.132339_bb0015","doi-asserted-by":"crossref","first-page":"214","DOI":"10.1080\/09537104.2016.1265924","article-title":"Platelet microvesicles in health and disease","volume":"28","author":"Melki","year":"2017","journal-title":"Platelets"},{"key":"10.1016\/j.ijcard.2024.132339_bb0020","doi-asserted-by":"crossref","first-page":"1283","DOI":"10.1161\/ATVBAHA.122.318177","article-title":"Regulated necrosis in atherosclerosis","volume":"42","author":"Puylaert","year":"2022","journal-title":"Arterioscler. Thromb. Vasc. Biol."},{"key":"10.1016\/j.ijcard.2024.132339_bb0025","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1136\/jcp.2006.044107","article-title":"Complicated atheromatous plaque as integral atherogenesis","volume":"60","author":"Agius","year":"2007","journal-title":"J. Clin. Pathol."},{"key":"10.1016\/j.ijcard.2024.132339_bb0030","doi-asserted-by":"crossref","first-page":"684","DOI":"10.1038\/s41569-022-00687-9","article-title":"Optical coherence tomography in coronary atherosclerosis assessment and intervention","volume":"19","author":"Araki","year":"2022","journal-title":"Nat. Rev. Cardiol."},{"key":"10.1016\/j.ijcard.2024.132339_bb0035","doi-asserted-by":"crossref","first-page":"873","DOI":"10.1007\/s12094-022-03014-3","article-title":"Platelet-derived microvesicles (PMVs) in cancer progression and clinical applications","volume":"25","author":"Pan","year":"2023","journal-title":"Clin. Transl. Oncol."},{"key":"10.1016\/j.ijcard.2024.132339_bb0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.bcmd.2022.102701","article-title":"Platelet-derived microvesicles activate human platelets via intracellular calcium mediated reactive oxygen species release","volume":"98","author":"Yadav","year":"2023","journal-title":"Blood Cells Mol. Dis."},{"key":"10.1016\/j.ijcard.2024.132339_bb0045","doi-asserted-by":"crossref","first-page":"5131","DOI":"10.2147\/JIR.S326716","article-title":"Platelet mediated inflammation in coronary artery disease with type 2 diabetes patients","volume":"14","author":"Johny","year":"2021","journal-title":"J. Inflamm. Res."},{"key":"10.1016\/j.ijcard.2024.132339_bb0050","doi-asserted-by":"crossref","first-page":"1138","DOI":"10.1007\/s11239-020-02302-5","article-title":"Platelet microvesicles are associated with the severity of coronary artery disease: comparison between peripheral and coronary circulation","volume":"51","author":"Gkaliagkousi","year":"2021","journal-title":"J. Thromb. Thrombolysis"},{"key":"10.1016\/j.ijcard.2024.132339_bb0055","doi-asserted-by":"crossref","first-page":"1095","DOI":"10.1016\/j.ccell.2022.09.012","article-title":"F. Artificial intelligence for multimodal data integration in oncology","volume":"40","author":"Lipkova","year":"2022","journal-title":"Cancer Cell"},{"key":"10.1016\/j.ijcard.2024.132339_bb0060","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1038\/s41586-023-05881-4","article-title":"Foundation models for generalist medical artificial intelligence","volume":"616","author":"Moor","year":"2023","journal-title":"Nature"},{"key":"10.1016\/j.ijcard.2024.132339_bb0065","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.949454","article-title":"Artificial intelligence in atherosclerotic disease: applications and trends","volume":"9","author":"Kampaktsis","year":"2023","journal-title":"Front Cardiovasc Med"},{"key":"10.1016\/j.ijcard.2024.132339_bb0070","doi-asserted-by":"crossref","first-page":"3609","DOI":"10.3934\/mbe.2022167","article-title":"FCM-DNN: diagnosing coronary artery disease by deep accuracy fuzzy C-means clustering model","volume":"19","author":"Joloudari","year":"2022","journal-title":"Math. Biosci. Eng."},{"key":"10.1016\/j.ijcard.2024.132339_bb0075","first-page":"6096289","article-title":"An AI-driven hybrid framework for intrusion detection in IoT-enabled E-health","author":"Wahab","year":"2022","journal-title":"Comput. Intell. Neurosci."},{"key":"10.1016\/j.ijcard.2024.132339_bb0080","doi-asserted-by":"crossref","first-page":"1105","DOI":"10.1038\/s41371-023-00854-6","article-title":"Circulating microvesicles across a population with various degrees of cardiovascular burden are associated with systolic blood pressure","author":"Gavriilaki","year":"2023","journal-title":"J. Hum. Hypertens."},{"key":"10.1016\/j.ijcard.2024.132339_bb0085","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1007\/BF00116251","article-title":"Induction of decision trees","volume":"1","author":"Quinlan","year":"1986","journal-title":"Mach. Learn."},{"key":"10.1016\/j.ijcard.2024.132339_bb0090","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1007\/s10994-006-6226-1","article-title":"Extremely randomized trees","volume":"63","author":"Geurts","year":"2006","journal-title":"Mach. Learn."},{"key":"10.1016\/j.ijcard.2024.132339_bb0095","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/S0167-9473(01)00065-2","article-title":"Stochastic gradient boosting","volume":"38","author":"Friedman","year":"2002","journal-title":"Comput Stat Data Anal"},{"key":"10.1016\/j.ijcard.2024.132339_bb0100","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1109\/34.735807","article-title":"Bayesian classification with Gaussian processes","volume":"20","author":"Williams","year":"1998","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"10.1016\/j.ijcard.2024.132339_bb0105","doi-asserted-by":"crossref","first-page":"561","DOI":"10.1038\/s41371-021-00531-6","article-title":"A study of endothelial and platelet microvesicles across different hypertension phenotypes","volume":"36","author":"Lazaridis","year":"2022","journal-title":"J. Hum. Hypertens."},{"key":"10.1016\/j.ijcard.2024.132339_bb0110","doi-asserted-by":"crossref","first-page":"109","DOI":"10.3390\/info12030109","article-title":"Analysis and prediction of COVID-19 using SIR, SEIQR, and machine learning models: Australia, Italy, and UK cases","volume":"12","author":"Rahimi","year":"2021","journal-title":"Information"},{"key":"10.1016\/j.ijcard.2024.132339_bb0115","doi-asserted-by":"crossref","unstructured":"Asteris, P. Douvika, M.G, Karamani, C.A., Skentou A.D, Chlichlia, K., Cavaleri, et al. A novel heuristic algorithm for the modeling and risk assessment of the COVID-19 pandemic phenomenon. CMES-COMP MODEL ENG 2020;125:815\u2013828.","DOI":"10.32604\/cmes.2020.013280"},{"key":"10.1016\/j.ijcard.2024.132339_bb0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.chaos.2021.111621","article-title":"Modeling the effect of the vaccination campaign on the COVID-19 pandemic","volume":"154","author":"Angeli","year":"2022","journal-title":"Chaos, Solitons Fractals"},{"key":"10.1016\/j.ijcard.2024.132339_bb0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.clim.2021.108726","article-title":"Genetic justification of severe COVID-19 using a rigorous algorithm","volume":"226","author":"Gavriilaki","year":"2021","journal-title":"Clin. Immunol."},{"key":"10.1016\/j.ijcard.2024.132339_bb0130","doi-asserted-by":"crossref","first-page":"11423","DOI":"10.3390\/app112311423","article-title":"COVID-19 patient detection based on fusion of transfer learning and fuzzy ensemble models using CXR images","volume":"11","author":"Mahanty","year":"2021","journal-title":"Appl. Sci."},{"issue":"26","key":"10.1016\/j.ijcard.2024.132339_bb0135","doi-asserted-by":"crossref","first-page":"1445","DOI":"10.1111\/jcmm.17098","article-title":"Genetic prediction of ICU hospitalization and mortality in COVID-19 patients using artificial neural networks","volume":"2022","author":"Asteris","year":"2022","journal-title":"J. Cell. Mol. Med."},{"key":"10.1016\/j.ijcard.2024.132339_bb0140","doi-asserted-by":"crossref","DOI":"10.1016\/j.clim.2022.109218","article-title":"Early prediction of COVID-19 outcome using artificial intelligence techniques and only five laboratory indices","volume":"246","author":"Asteris","year":"2023","journal-title":"Clin. Immunol."}],"container-title":["International Journal of Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527324009616?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527324009616?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2024,7,19]],"date-time":"2024-07-19T00:21:50Z","timestamp":1721348510000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167527324009616"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10]]},"references-count":28,"alternative-id":["S0167527324009616"],"URL":"https:\/\/doi.org\/10.1016\/j.ijcard.2024.132339","relation":{},"ISSN":["0167-5273"],"issn-type":[{"value":"0167-5273","type":"print"}],"subject":[],"published":{"date-parts":[[2024,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Revealing the nature of cardiovascular disease using DERGA, a novel data ensemble refinement greedy algorithm","name":"articletitle","label":"Article Title"},{"value":"International Journal of Cardiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijcard.2024.132339","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2024 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132339"}}