{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,11]],"date-time":"2026-01-11T03:57:18Z","timestamp":1768103838453,"version":"3.49.0"},"reference-count":42,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2020,10,1]],"date-time":"2020-10-01T00:00:00Z","timestamp":1601510400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Clinica Chimica Acta"],"published-print":{"date-parts":[[2020,10]]},"DOI":"10.1016\/j.cca.2020.06.001","type":"journal-article","created":{"date-parts":[[2020,6,4]],"date-time":"2020-06-04T11:17:55Z","timestamp":1591269475000},"page":"67-71","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":37,"special_numbering":"C","title":["Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine"],"prefix":"10.1016","volume":"509","author":[{"given":"Damien","family":"Gruson","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergio","family":"Bernardini","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pradeep Kumar","family":"Dabla","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bernard","family":"Gouget","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sanja","family":"Stankovic","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"key":"10.1016\/j.cca.2020.06.001_b0005","doi-asserted-by":"crossref","unstructured":"R.F. Greaves, S. Bernardini, M. Ferrari, P. Fortina, B. Gouget, D. Gruson et al., Key questions about the future of laboratory medicine in the next decade of the 21st century: A report from the IFCC-Emerging Technologies Division, Clin. Chim. Acta [Internet], 2019 Aug [cited 2019 Jul 24];495:570\u2013589. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31145895.","DOI":"10.1016\/j.cca.2019.05.021"},{"key":"10.1016\/j.cca.2020.06.001_b0010","unstructured":"A. Timmis, N. Townsend, C.P. Gale, A. Torbica, M. Lettino, S.E. Petersen et al., European Society of Cardiology: Cardiovascular Disease Statistics 2019, Eur. Heart J. [Internet], 2020 Jan 1 [cited 2020 Mar 17];41(1),12\u201385. Available from: https:\/\/academic.oup.com\/eurheartj\/article\/41\/1\/12\/5670482."},{"key":"10.1016\/j.cca.2020.06.001_b0015","unstructured":"E.J. Benjamin, P. Muntner, A. Alonso, M.S. Bittencourt, C.W. Callaway, A.P. Carson et al., Heart disease and stroke statistics\u20142019 update: a report from the American Heart Association, Circulation [Internet], 2019 Mar 5 [cited 2020 Mar 17];139(10). Available from: https:\/\/www.ahajournals.org\/doi\/10.1161\/CIR.0000000000000659."},{"key":"10.1016\/j.cca.2020.06.001_b0020","unstructured":"CVD Statistics [Internet], [cited 2020 Mar 17]. Available from: http:\/\/www.ehnheart.org\/cvd-statistics.html."},{"key":"10.1016\/j.cca.2020.06.001_b0025","doi-asserted-by":"crossref","unstructured":"K. Thygesen, J.S. Alpert, A.S. Jaffe, B.R. Chaitman, J.J. Bax, D.A. Morrow et al., Fourth universal definition of myocardial infarction (2018), J. Am. Coll. Cardiol. [Internet]. 2018 Oct 30 [cited 2020 Mar 17];72(18):2231\u20132264. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30153967.","DOI":"10.1016\/j.jacc.2018.08.1038"},{"key":"10.1016\/j.cca.2020.06.001_b0030","doi-asserted-by":"crossref","unstructured":"P. Ponikowski, A.A. Voors, S.D. Anker, H. Bueno, J.G.F. Cleland, A.J.S. Coats et al., 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure, Eur. J. Heart Fail [Internet], 2016 Aug [cited 2020 Mar 17];18(8):891\u2013975. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/27207191.","DOI":"10.1002\/ejhf.592"},{"key":"10.1016\/j.cca.2020.06.001_b0035","doi-asserted-by":"crossref","unstructured":"D. Gruson, T. Helleputte, P. Rousseau, D. Gruson, Data science, artificial intelligence, and machine learning: Opportunities for laboratory medicine and the value of positive regulation, vol. 69, Clinical Biochemistry. Elsevier Inc., 2019, p. 1\u20137.","DOI":"10.1016\/j.clinbiochem.2019.04.013"},{"issue":"9","key":"10.1016\/j.cca.2020.06.001_b0040","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1007\/s12471-019-01311-1","article-title":"Artificial intelligence in cardiovascular imaging: state of the art and implications for the imaging cardiologist","volume":"27","author":"Siegersma","year":"2019","journal-title":"Neth. Heart J."},{"key":"10.1016\/j.cca.2020.06.001_b0045","unstructured":"Y. Yan, J.-W. Zhang, G.-Y. Zang, J. Pu, The primary use of artificial intelligence in cardiovascular diseases: what kind of potential role does artificial intelligence play in future medicine?, J. Geriatr. Cardiol. [Internet], 2019 Aug [cited 2020 Feb 2];16(8):585\u2013591. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31555325."},{"key":"10.1016\/j.cca.2020.06.001_b0050","unstructured":"L.M. Fleuren, T.L.T. Klausch, C.L. Zwager, L.J. Schoonmade, T. Guo, L.F. Roggeveen et al., Machine learning for the prediction of sepsis: a systematic review and meta-analysis of diagnostic test accuracy, Intensive Care Med. [Internet]. 2020 Jan 21 [cited 2020 Feb 2]; Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31965266."},{"key":"10.1016\/j.cca.2020.06.001_b0055","doi-asserted-by":"crossref","unstructured":"S.A. Niederer, J. Lumens, N.A. Trayanova, Computational models in cardiology, Nat. Rev. Cardiol. [Internet], 2019 Feb 25 [cited 2020 Mar 18];16(2):100\u2013111. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30361497.","DOI":"10.1038\/s41569-018-0104-y"},{"key":"10.1016\/j.cca.2020.06.001_b0060","doi-asserted-by":"crossref","unstructured":"W. Chua, Y. Purmah, V.R. Cardoso, G.V. Gkoutos, S.P. Tull, G. Neculau et al., Data-driven discovery and validation of circulating blood-based biomarkers associated with prevalent atrial fibrillation, Eur. Heart J. [Internet], 2019 Apr 21 [cited 2020 Feb 2];40(16):1268\u20131276. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30615112.","DOI":"10.1093\/eurheartj\/ehy815"},{"key":"10.1016\/j.cca.2020.06.001_b0065","doi-asserted-by":"crossref","unstructured":"S.T. Vernon, T. Hansen, K.A. Kott, J.Y. Yang, J.F. O\u2019Sullivan, G.A. Figtree, Utilizing state-of-the-art "omics" technology and bioinformatics to identify new biological mechanisms and biomarkers for coronary artery disease. Microcirculation [Internet], 2019 [cited 2020 Feb 2];26(2):e12488. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/29956866.","DOI":"10.1111\/micc.12488"},{"key":"10.1016\/j.cca.2020.06.001_b0070","unstructured":"A.M. Poss, J.A. Maschek, J.E. Cox, B.J. Hauner, P.N. Hopkins, S.C. Hunt et al., Machine learning reveals serum sphingolipids as cholesterol-independent biomarkers of coronary artery disease, J. Clin. Invest. [Internet], 2020 Jan 27 [cited 2020 Feb 2]; Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31743112."},{"issue":"1","key":"10.1016\/j.cca.2020.06.001_b0075","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-56889-8","article-title":"Machine-learning facilitates selection of a novel diagnostic panel of metabolites for the detection of heart failure","volume":"10","author":"Marcinkiewicz-Siemion","year":"2020","journal-title":"Sci. Rep."},{"key":"10.1016\/j.cca.2020.06.001_b0080","doi-asserted-by":"crossref","unstructured":"G. Captur, W.E. Heywood, C. Coats, S. Rosmini, V. Patel, L.R. Lopes et al., Identification of a multiplex biomarker panel for hypertrophic cardiomyopathy using quantitative proteomics and machine learning, Mol. Cell Proteomics [Internet], 2020 Jan [cited 2020 Feb 2];19(1):114\u2013127. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31243064.","DOI":"10.1074\/mcp.RA119.001586"},{"key":"10.1016\/j.cca.2020.06.001_b0085","doi-asserted-by":"crossref","unstructured":"K.A. Kott, S.T. Vernon, T. Hansen, C. Yu, K.J. Bubb, S. Coffey et al., Biobanking for discovery of novel cardiovascular biomarkers using imaging-quantified disease burden: protocol for the longitudinal, prospective, BioHEART-CT cohort study, BMJ Open [Internet], 2019 Sep 18 [cited 2020 Feb 2];9(9):e028649. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31537558.","DOI":"10.1136\/bmjopen-2018-028649"},{"key":"10.1016\/j.cca.2020.06.001_b0090","doi-asserted-by":"crossref","unstructured":"S.F. Weng, J. Reps, J. Kai, J.M. Garibaldi, N. Qureshi, Can machine-learning improve cardiovascular risk prediction using routine clinical data?, PLoS One [Internet], 2017 [cited 2020 Feb 2];12(4):e0174944. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28376093.","DOI":"10.1371\/journal.pone.0174944"},{"key":"10.1016\/j.cca.2020.06.001_b0095","doi-asserted-by":"crossref","unstructured":"B. Ambale-Venkatesh, X. Yang, C.O. Wu, K. Liu, W.G. Hundley, R. McClelland et al., Cardiovascular event prediction by machine learning: the multi-ethnic study of atherosclerosis, Circ. Res. [Internet]. 2017 Oct 13 [cited 2020 Feb 2];121(9):1092\u20131101. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/28794054.","DOI":"10.1161\/CIRCRESAHA.117.311312"},{"key":"10.1016\/j.cca.2020.06.001_b0100","doi-asserted-by":"crossref","unstructured":"M.V. Dogan, S.R.H. Beach, R.L. Simons, A. Lendasse, B. Penaluna, R.A. Philibert, Blood-based biomarkers for predicting the risk for five-year incident coronary heart disease in the framingham heart study via machine learning, Genes (Basel) [Internet], 2018 Dec 18 [cited 2020 Feb 2];9(12). Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30567402.","DOI":"10.3390\/genes9120641"},{"key":"10.1016\/j.cca.2020.06.001_b0105","doi-asserted-by":"crossref","unstructured":"Q.A. Hathaway, S.M. Roth, M.V. Pinti, D.C. Sprando, A. Kunovac, A.J. Durr et al., Machine-learning to stratify diabetic patients using novel cardiac biomarkers and integrative genomics, Cardiovasc. Diabetol. [Internet], 2019 [cited 2020 Feb 2];18(1):78. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31185988.","DOI":"10.1186\/s12933-019-0879-0"},{"key":"10.1016\/j.cca.2020.06.001_b0110","doi-asserted-by":"crossref","unstructured":"S. Mezzatesta, C. Torino, P. De Meo, G. Fiumara, A. Vilasi, A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis, Comput. Methods Programs Biomed. [Internet], 2019 Aug [cited 2020 Feb 2];177:9\u201315. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31319965.","DOI":"10.1016\/j.cmpb.2019.05.005"},{"key":"10.1016\/j.cca.2020.06.001_b0115","doi-asserted-by":"crossref","unstructured":"G.B. Berikol, O. Yildiz, I.T. \u00d6zcan, Diagnosis of acute coronary syndrome with a support vector machine, J. Med. Syst. [Internet], 2016 Apr [cited 2020 Feb 2];40(4):84. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26815338.","DOI":"10.1007\/s10916-016-0432-6"},{"key":"10.1016\/j.cca.2020.06.001_b0120","doi-asserted-by":"crossref","unstructured":"C.-C. Wu, W.-D. Hsu, M.M. Islam, T.N. Poly, H.-C. Yang, P.-A.A. Nguyen et al., An artificial intelligence approach to early predict non-ST-elevation myocardial infarction patients with chest pain, Comput. Methods Programs Biomed. [Internet], 2019 May [cited 2020 Feb 2];173:109\u2013117. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31046985.","DOI":"10.1016\/j.cmpb.2019.01.013"},{"key":"10.1016\/j.cca.2020.06.001_b0125","doi-asserted-by":"crossref","unstructured":"M.P. Than, J.W. Pickering, Y. Sandoval, A.S.V. Shah, A. Tsanas, F.S. Apple et al., Machine learning to predict the likelihood of acute myocardial infarction, Circulation [Internet], 2019 Aug 16 [cited 2020 Feb 2];140(11). Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31416346.","DOI":"10.1161\/CIRCULATIONAHA.119.041980"},{"key":"10.1016\/j.cca.2020.06.001_b0130","unstructured":"\u00c5.K. Hedman, C. Hage, A. Sharma, M.J. Brosnan, L. Buckbinder, L.-M. Gan et al., Identification of novel pheno-groups in heart failure with preserved ejection fraction using machine learning, Heart [Internet], 2020 Jan 7 [cited 2020 Feb 2]; Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31911501."},{"key":"10.1016\/j.cca.2020.06.001_b0135","doi-asserted-by":"crossref","unstructured":"M. Cikes, S. Sanchez-Martinez, B. Claggett, N. Duchateau, G. Piella, C. Butakoff et al., Machine learning-based phenogrouping in heart failure to identify responders to cardiac resynchronization therapy, Eur. J. Heart Fail [Internet], 2019 [cited 2020 Feb 2];21(1):74\u201385. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30328654.","DOI":"10.1002\/ejhf.1333"},{"key":"10.1016\/j.cca.2020.06.001_b0140","doi-asserted-by":"crossref","unstructured":"R.L. McNamara, E.S. Spatz, T.A. Kelley, C.J. Stowell, J. Beltrame, P. Heidenreich et al., Standardized outcome measurement for patients with coronary artery disease: consensus from the international consortium for health outcomes measurement (ICHOM), J. Am. Heart Assoc. [Internet], 2015 May 19 [cited 2020 Mar 17];4(5). Available from: https:\/\/www.ahajournals.org\/doi\/10.1161\/JAHA.115.001767.","DOI":"10.1161\/JAHA.115.001767"},{"key":"10.1016\/j.cca.2020.06.001_b0145","doi-asserted-by":"crossref","unstructured":"D.J.P. Burns, J. Arora, O. Okunade, J.F. Beltrame, S. Bernardez-Pereira, M.G. Crespo-Leiro et al., International consortium for health outcomes measurement (ICHOM): standardized patient-centered outcomesmeasurement set for heartfailure patients, JACC Hear Fail [Internet], 2020 Mar 1 [cited 2020 Mar 17];8(3):212\u201322. Available from: https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2213177919307978.","DOI":"10.1016\/j.jchf.2019.09.007"},{"key":"10.1016\/j.cca.2020.06.001_b0150","unstructured":"K. Shameer, K.W. Johnson, A. Yahi, R. Miotto, L.I. Li, D. Ricks et al., Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using mount sinai heart failure cohort, Pac. Symp. Biocomput. [Internet], 2017 [cited 2020 Feb 2];22:276\u201387. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/27896982."},{"key":"10.1016\/j.cca.2020.06.001_b0155","doi-asserted-by":"crossref","unstructured":"D.H. Brahmbhatt, M.R. Cowie, Remote management of heart failure: an overview of telemonitoring technologies, Card Fail Rev. [Internet], 2019 May [cited 2020 Feb 2];5(2):86\u201392. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31179018.","DOI":"10.15420\/cfr.2019.5.3"},{"key":"10.1016\/j.cca.2020.06.001_b0160","unstructured":"Defining value in \u2018value-based healthcare\u2019 - Publications Office of the EU [Internet], [cited 2020 Mar 18], Available from: https:\/\/op.europa.eu\/en\/publication-detail\/-\/publication\/d7087e5e-ac2b-11e9-9d01-01aa75ed71a1."},{"key":"10.1016\/j.cca.2020.06.001_b0165","doi-asserted-by":"crossref","unstructured":"S.J. Matkovich, Multiomic approaches to delineate the pathogenesis of cardiac disease, Curr. Opin. Cardiol. [Internet], 2019 May [cited 2020 Apr 25];34(3):246\u201353. Available from: http:\/\/insights.ovid.com\/crossref?an=00001573-201905000-00003.","DOI":"10.1097\/HCO.0000000000000611"},{"key":"10.1016\/j.cca.2020.06.001_b0170","doi-asserted-by":"crossref","unstructured":"G. de Anda-J\u00e1uregui, E. Hern\u00e1ndez-Lemus, Computational oncology in the multi-omics era: state of the art, Front. Oncol. [Internet], 2020 Apr 7 [cited 2020 Apr 25];10:423. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/32318338.","DOI":"10.3389\/fonc.2020.00423"},{"key":"10.1016\/j.cca.2020.06.001_b0175","doi-asserted-by":"crossref","unstructured":"A. Hure, K. Palazzi, R. Peel, D. Geraghty, P. Collard, T. De Malmanche et al., Identifying low value pathology test ordering in hospitalised patients: a retrospective cohort study across two hospitals, Pathology [Internet], 2019 Oct [cited 2020 Feb 28];51(6):621\u2013627. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31472983.","DOI":"10.1016\/j.pathol.2019.06.003"},{"key":"10.1016\/j.cca.2020.06.001_b0180","doi-asserted-by":"crossref","unstructured":"R. Harb, D. Hajdasz, M.L. Landry, L.S. Sussman, Improving laboratory test utilisation at the multihospital Yale New Haven Health System, BMJ Open Qual. [Internet], 2019 [cited 2020 Feb 28];8(3):e000689. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31637323.","DOI":"10.1136\/bmjoq-2019-000689"},{"issue":"3","key":"10.1016\/j.cca.2020.06.001_b0185","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1038\/s41551-018-0195-0","article-title":"Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning","volume":"2","author":"Poplin","year":"2018","journal-title":"Nat. Biomed. Eng."},{"key":"10.1016\/j.cca.2020.06.001_b0190","doi-asserted-by":"crossref","unstructured":"I. Frederix, E.G. Caiani, P. Dendale, S. Anker, J. Bax, A. B\u00f6hm et al., ESC e-Cardiology Working Group Position Paper: Overcoming challenges in digital health implementation in cardiovascular medicine, Eur. J. Prev. Cardiol. [Internet], 2019 Jul [cited 2020 Feb 2];26(11):1166\u201377. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/30917695.","DOI":"10.1177\/2047487319832394"},{"key":"10.1016\/j.cca.2020.06.001_b0195","doi-asserted-by":"crossref","unstructured":"M.R. Cowie, Exploring digital technology\u2019s potential for cardiology, Eur. Heart J. [Internet], 2019 Jul 21 [cited 2020 Feb 2];40(28):2283\u20134. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/31327009.","DOI":"10.1093\/eurheartj\/ehz490"},{"key":"10.1016\/j.cca.2020.06.001_b0200","doi-asserted-by":"crossref","unstructured":"M.R. Cowie, J. Bax, N. Bruining, J.G.F. Cleland, F. Koehler, M. Malik et al., e-Health: a position statement of the European Society of Cardiology, Eur. Heart J. [Internet], 2016 Jan 1 [cited 2020 Feb 2];37(1):63\u20136. Available from: http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/26303835.","DOI":"10.1093\/eurheartj\/ehv416"},{"key":"10.1016\/j.cca.2020.06.001_b0205","unstructured":"Ethics guidelines for trustworthy AI | Shaping Europe\u2019s digital future [Internet], [cited 2020 May 1]. Available from: https:\/\/ec.europa.eu\/digital-single-market\/en\/news\/ethics-guidelines-trustworthy-ai."},{"key":"10.1016\/j.cca.2020.06.001_b0210","unstructured":"Artificial Intelligence | Shaping Europe\u2019s digital future [Internet], [cited 2020 Mar 18], Available from: https:\/\/ec.europa.eu\/digital-single-market\/en\/artificial-intelligence."}],"container-title":["Clinica Chimica Acta"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0009898120302655?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0009898120302655?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,9,14]],"date-time":"2025-09-14T14:53:41Z","timestamp":1757861621000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0009898120302655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10]]},"references-count":42,"alternative-id":["S0009898120302655"],"URL":"https:\/\/doi.org\/10.1016\/j.cca.2020.06.001","relation":{},"ISSN":["0009-8981"],"issn-type":[{"value":"0009-8981","type":"print"}],"subject":[],"published":{"date-parts":[[2020,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Collaborative AI and Laboratory Medicine integration in precision cardiovascular medicine","name":"articletitle","label":"Article Title"},{"value":"Clinica Chimica Acta","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.cca.2020.06.001","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2020 Elsevier B.V. All rights reserved.","name":"copyright","label":"Copyright"}]}}