1. Davidson, E, Baird, A, Prince, K. Opening the envelope of health care information systems research. Inf Organ 2018; 28: 140–151.
Google Scholar | Crossref2. Hu, Y, Duan, K, Zhang, Y, et al. Simultaneously aided diagnosis model for outpatient departments via healthcare big data analytics. Multimed Tools Appl 2018; 77: 3729–3743.
Google Scholar | Crossref3. Bates, DW, Heitmueller, A, Kakad, M, et al. Why policymakers should care about “big data” in healthcare. Health Policy Technol 2018; 7: 211–216.
Google Scholar | Crossref4. Lucyk, K, Lu, M, Sajobi, T, et al. Administrative health data in Canada: lessons from history. BMC Med Inform Decis Mak 2015; 15: 69.
Google Scholar | Crossref | Medline5. Holman, CDJ, Bass, JA, Rosman, DL, et al. A decade of data linkage in Western Australia: strategic design, applications and benefits of the WA data linkage system. Aust Health Rev 2008; 32: 766–777.
Google Scholar | Crossref | Medline | ISI6. Tuppin, P, de Roquefeuil, L, Weill, A, et al. French national health insurance information system and the permanent beneficiaries sample. Rev Épidémiol Santé Publique 2010; 58: 286–290.
Google Scholar | Crossref | Medline | ISI7. Moulis, G, Lapeyre-Mestre, M, Palmaro, A, et al. French health insurance databases: what interest for medical research? Rev Méd Interne 2015; 36: 411–417.
Google Scholar | Crossref | Medline | ISI8. Tuppin, P, Rudant, J, Constantinou, P, et al. Value of a national administrative database to guide public decisions: from the système national d’information interrégimes de l’Assurance Maladie (SNIIRAM) to the système national des données de santé (SNDS) in France. Rev Épidémiol Santé Publique 2017; 65: S149–S167.
Google Scholar | Crossref | Medline9. Arndt, H. Knowledge discovery and anomalies — towards a dynamic decision-making model for medical informatics. Thesis, Stellenbosch University, Stellenbosch, 2018. https://scholar.sun.ac.za:443/handle/10019.1/103311
Google Scholar10. World Health Organization . New initiative launched to tackle cardiovascular disease, the world’s number one killer, http://www.who.int/cardiovascular_diseases/global-hearts/Global_hearts_initiative/en/ (2011, accessed 22 September 2016).
Google Scholar11. Mendis, S, Puska, P, Norrving, B, et al. Global atlas on cardiovascular disease prevention and control. Geneva: World Health Organization, 2011. http://www.who.int/cardiovascular_diseases/publications/atlas_cvd/en/
Google Scholar12. Townsend, N, Wilson, L, Bhatnagar, P, et al. Cardiovascular disease in Europe: epidemiological update 2016. Eur Heart J 2016; 37: 3232–3245.
Google Scholar | Crossref | Medline | ISI13. Shaw, LJ, Bugiardini, R, Merz, CNB. Women and ischemic heart disease: evolving knowledge. J Am Coll Cardiol 2009; 54: 1561–1575.
Google Scholar | Crossref | Medline | ISI14. Pinaire, J, Azé, J, Bringay, S, et al. Hospital burden of coronary artery disease: trends of myocardial infarction and/or percutaneous coronary interventions in France 2009–2014. PLoS One 2019; 14: e0215649.
Google Scholar | Crossref | Medline15. Roffi, M, Patrono, C, Collet, J-P, et al. 2015 ESC Guidelines for the management of acute coronary syndromes in patients presenting without persistent ST-segment elevation: task force for the Management of Acute Coronary Syndromes in patients presenting without persistent ST-segment elevation of the European Society of Cardiology (ESC). Eur Heart J 2016; 37: 267–315.
Google Scholar | Crossref | Medline | ISI16. Phan, N, Poncelet, P, Teisseire, M. All in one: mining multiple movement patterns. Int J Inf Technol Decis Mak 2016; 15: 1115–1156.
Google Scholar | Crossref17. Phan, NH, Ienco, D, Poncelet, P, et al. Mining fuzzy moving object clusters. In: Advanced data mining and applications – 8th international conference, ADMA 2012, Nanjing, China, , pp.100–114. Springer New-York, USA.
Google Scholar18. Melnychuk, MC, Welch, DW, Walters, CJ. Spatio-temporal migration patterns of Pacific Salmon Smolts in rivers and coastal marine waters. PLoS One 2010; 5: e12916.
Google Scholar | Crossref | Medline19. Wilson, RE. Mechanisms for spatio-temporal pattern formation in highway traffic models. Philos Trans R Soc Lond Math Phys Eng Sci 2008; 366: 2017–2032.
Google Scholar | Crossref | Medline20. Mao, F, Ji, M, Liu, T. Mining spatiotemporal patterns of urban dwellers from taxi trajectory data. Front Earth Sci 2016; 10: 205–221.
Google Scholar | Crossref21. Genolini, C, Ecochard, R, Benghezal, M, et al. kmlShape: an efficient method to cluster longitudinal data (time-series) according to their shapes. PLoS One 2016; 11: e0150738.
Google Scholar | Crossref | Medline22. Seeker, LA, Ilska, JJ, Psifidi, A, et al. Longitudinal changes in telomere length and associated genetic parameters in dairy cattle analysed using random regression models. PLoS One 2018; 13: e0192864.
Google Scholar | Crossref | Medline23. Abdulla, S, Bouchard, T, Klich, A, et al. The use of beta-binomial distributions to describe hormone profiles in the normal menstrual cycle. Rev Épidémiol Santé Publique 2017; 65: S69–S70.
Google Scholar | Crossref24. Pinaire, J, Azé, J, Bringay, S, et al. PaFloChar: an innovating approach to characterise patient flows in myocardial infarction. In: Building continents of knowledge in oceans of data: the future of co-created EHealth. Göteborg, 2018, pp.391–395. IOS Press.
Google Scholar25. Quantin, C, Fassa, M, Coatrieux, G, et al. Linking anonymous databases for national and international multicenter epidemiological studies: a cryptographic algorithm. Rev Épidémiol Santé Publique 2009; 57: 33–39.
Google Scholar | Crossref | Medline26. Pédrono, G, Nectoux, M, Mugnier, C, et al. French home and leisure injury permanent survey: what contribution to epidemiological surveillance? Rev Épidémiol Santé Publique 2018; 66: S336.
Google Scholar | Crossref27. Pagès, P-B, Mariet, A-S, Pforr, A, et al. Does age over 80 years have to be a contraindication for lung cancer surgery—a nationwide database study. J Thorac Dis 2018; 10: 4764–4773.
Google Scholar | Crossref | Medline28. Thygesen, K, Alpert, JS, White, HD. Universal definition of myocardial infarction. J Am Coll Cardiol 2007; 50: 2173–2195.
Google Scholar | Crossref | Medline | ISI29. Genolini, C, Falissard, B. KmL: k-means for longitudinal data. Comput Stat 2010; 25: 317–328.
Google Scholar | Crossref | ISI30. Pingault, J-B, Côté, SM, Galéra, C, et al. Childhood trajectories of inattention, hyperactivity and oppositional behaviors and prediction of substance abuse/dependence: a 15-year longitudinal population-based study. Mol Psychiatry 2013; 18: 806–812.
Google Scholar | Crossref | Medline31. Genolini, C, Falissard, B. Kml: a package to cluster longitudinal data. Comput Methods Programs Biomed 2011; 104: e112–e121.
Google Scholar | Crossref | Medline | ISI32. WHO . International Classification of Diseases, 11th revision (ICD-11). Geneva: World Health Organization, 2004. http://www.who.int/classifications/icd/en/
Google Scholar33. Sanchis-Gomar, F, Perez-Quilis, C, Leischik, R, et al. Epidemiology of coronary heart disease and acute coronary syndrome. Ann Transl Med 2016; 4: 7.
Google Scholar | Crossref | Medline34. Batten, A, Jaeger, C, Griffen, D, et al. See you in 7: improving acute myocardial infarction follow-up care. BMJ Open Qual 2018; 7: e000296.
Google Scholar | Crossref | Medline35. Dharmarajan, K, Hsieh, AF, Kulkarni, VT, et al. Trajectories of risk after hospitalization for heart failure, acute myocardial infarction, or pneumonia: retrospective cohort study. BMJ 2015; 350: h411.
Google Scholar | Crossref | Medline36. Schapiro-Dufour, E, Cucherat, M, Velzenberger, E, et al. Drug-eluting stents in patients at high risk of restenosis: assessment for France. Int J Technol Assess Health Care 2011; 27: 108–117.
Google Scholar | Crossref | Medline37. Kim, LK, Yeo, I, Cheung, JW, et al. Thirty-day readmission rates, timing, causes, and costs after ST-segment–elevation myocardial infarction in the United States: a national readmission database analysis 2010–2014. J Am Heart Assoc 2018; 7: e009863.
Google Scholar | Crossref | Medline38. Sacco, S, Pistoia, F, Carolei, A. Stroke tracked by administrative coding data. Stroke 2013; 44: 1766–1768.
Google Scholar | Crossref | Medline | ISI39. Le Manach, Y, Collins, G, Rodseth, R, et al. Preoperative Score to Predict Postoperative Mortality (POSPOM): derivation and validation. Anesthesiology 2016; 124: 570–579.
Google Scholar | Crossref | Medline40. Chauvet-Gelinier, J-C, Roussot, A, Cottenet, J, et al. Depression and obesity, data from a national administrative database study: geographic evidence for an epidemiological overlap. PLoS One 2019; 14: e0210507.
Google Scholar | Crossref | Medline41. Garnier, F, Couchoud, C, Landais, P, et al. Increased incidence of acute kidney injury requiring dialysis in metropolitan France. PLoS One 2019; 14: e0211541.
Google Scholar | Crossref | Medline42. Miller, AL, Simon, D, Roe, MT, et al. Comparison of delay times from symptom onset to medical contact in blacks versus whites with acute myocardial infarction. Am J Cardiol 2017; 119: 1127–1134.
Google Scholar | Crossref | Medline43. Blin, P, Dureau-Pournin, C, Lassalle, R, et al. Outcomes, health care resources use, and costs in patients with post-myocardial infarction: the Horus Cohort Study in the Egb French claims and hospital database. Value Health 2016; 19: A16.
Google Scholar | Crossref44. Dégano, IR, Salomaa, V, Veronesi, G, et al. Twenty-five-year trends in myocardial infarction attack and mortality rates, and case-fatality, in six European populations. Heart 2015; 101: 1413–1421.
Google Scholar | Crossref | Medline45. Beck, MK, Jensen, AB, Nielsen, AB, et al. Diagnosis trajectories of prior multi-morbidity predict sepsis mortality. Sci Rep 2016; 6: 1–9.
Google Scholar | Crossref | Medline46. Zhang, Y, Padman, R, Patel, N. Paving the COWpath: learning and visualizing clinical pathways from electronic health record data. J Biomed Inform 2015; 58: 186–197.
Google Scholar | Crossref | Medline47. Funkner, AA, Yakovlev, AN, Kovalchuk, SV. Data-driven modeling of clinical pathways using electronic health records. Procedia Comput Sci 2017; 121: 835–842.
Google Scholar | Crossref48. Perer, A, Wang, F, Hu, J. Mining and exploring care pathways from electronic medical records with visual analytics. J Biomed Inform 2015; 56: 369–378.
Google Scholar | Crossref | Medline49. Giannoula, A, Gutierrez-Sacristán, A, Bravo, Á, et al. Identifying temporal patterns in patient disease trajectories using dynamic time warping: a population-based study. Sci Rep 2018; 8: 1–14.
Google Scholar | Crossref | Medline50. Perer, A, Sun, J. MatrixFlow: temporal network visual analytics to track symptom evolution during disease progression. AMIA Annu Symp Proc 2012; 2012: 716–725.
Google Scholar | Medline51. Rebuge, Á, Ferreira, DR. Business process analysis in healthcare environments: a methodology based on process mining. Inf Syst 2012; 37: 99–116.
Google Scholar | Crossref52. Defossez, G, Rollet, A, Dameron, O, et al.