{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,19]],"date-time":"2026-02-19T02:40:24Z","timestamp":1771468824797,"version":"3.50.1"},"reference-count":65,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T00:00:00Z","timestamp":1586476800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T00:00:00Z","timestamp":1586476800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Big Data"],"published-print":{"date-parts":[[2020,12]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>This paper proposes a novel algorithm for optimizing decision variables with respect to an outcome variable of interest in complex problems, such as those arising from big data. The proposed algorithm builds on the notion of Markov blankets in Bayesian networks to alleviate the computational challenges associated with optimization tasks in complex datasets. Through a case study, we apply the algorithm to optimize medication prescriptions for diabetic patients, who have different characteristics, suffer from multiple comorbidities, and take multiple medications concurrently. In particular, we demonstrate how the optimal combination of diabetic medications can be found by examining the comparative effectiveness of the medications among similar patients. The case study is based on 5 years of data for 19,223 diabetic patients. Our results indicate that certain patient characteristics (e.g., clinical and demographic features) influence optimal treatment decisions. Among patients examined, monotherapy with metformin was the most common optimal medication decision. The results are consistent with the relevant clinical guidelines and reports in the medical literature. The proposed algorithm obviates the need for knowledge of the whole Bayesian network model, which can be very complex in big data problems. The procedure can be applied to any complex Bayesian network with numerous features, multiple decision variables, and a target variable.<\/jats:p>","DOI":"10.1186\/s40537-020-00302-z","type":"journal-article","created":{"date-parts":[[2020,4,10]],"date-time":"2020-04-10T14:02:52Z","timestamp":1586527372000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":20,"title":["Leveraging machine learning and big data for optimizing medication prescriptions in complex diseases: a case study in diabetes management"],"prefix":"10.1186","volume":"7","author":[{"given":"Mahsa Madani","family":"Hosseini","sequence":"first","affiliation":[]},{"given":"Manaf","family":"Zargoush","sequence":"additional","affiliation":[]},{"given":"Farrokh","family":"Alemi","sequence":"additional","affiliation":[]},{"given":"Raya Elfadel","family":"Kheirbek","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,4,10]]},"reference":[{"key":"302_CR1","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/S0140-6736(17)32812-X","volume":"391","author":"JD Lee","year":"2018","unstructured":"Lee JD, Nunes EV Jr, Novo P, Bachrach K, Bailey GL, Bhatt S, et al. Comparative effectiveness of extended-release naltrexone versus buprenorphine-naloxone for opioid relapse prevention (X: BOT): a multicentre, open-label, randomised controlled trial. Lancet. 2018;391:309\u201318.","journal-title":"Lancet"},{"key":"302_CR2","doi-asserted-by":"publisher","first-page":"1049","DOI":"10.1001\/jamapediatrics.2017.3036","volume":"171","author":"Z Wang","year":"2017","unstructured":"Wang Z, Whiteside SP, Sim L, Farah W, Morrow AS, Alsawas M, et al. Comparative effectiveness and safety of cognitive behavioral therapy and pharmacotherapy for childhood anxiety disorders: a systematic review and meta-analysis. JAMA Pediatr. 2017;171:1049\u201356.","journal-title":"JAMA Pediatr"},{"key":"302_CR3","doi-asserted-by":"publisher","first-page":"110","DOI":"10.7326\/M17-1805","volume":"168","author":"KT Mills","year":"2018","unstructured":"Mills KT, Obst KM, Shen W, Molina S, Zhang H-J, He H, et al. Comparative effectiveness of implementation strategies for blood pressure control in hypertensive patients: a systematic review and meta-analysis. Ann Intern Med. 2018;168:110\u201320.","journal-title":"Ann Intern Med"},{"key":"302_CR4","doi-asserted-by":"publisher","first-page":"788","DOI":"10.1016\/j.amepre.2017.01.008","volume":"52","author":"MJ O\u2019Brien","year":"2017","unstructured":"O\u2019Brien MJ, Perez A, Scanlan AB, Alos VA, Whitaker RC, Foster GD, et al. PREVENT-DM comparative effectiveness trial of lifestyle intervention and metformin. Am J Prev Med. 2017;52:788\u201397.","journal-title":"Am J Prev Med"},{"key":"302_CR5","doi-asserted-by":"publisher","first-page":"12680","DOI":"10.1038\/s41598-017-12987-z","volume":"7","author":"SWH Lee","year":"2017","unstructured":"Lee SWH, Chan CKY, Chua SS, Chaiyakunapruk N. Comparative effectiveness of telemedicine strategies on type 2 diabetes management: a systematic review and network meta-analysis. Sci Rep. 2017;7:12680.","journal-title":"Sci Rep"},{"key":"302_CR6","doi-asserted-by":"publisher","first-page":"6","DOI":"10.2337\/dc17-1223","volume":"41","author":"S Suissa","year":"2018","unstructured":"Suissa S. Lower risk of death with SGLT2 inhibitors in observational studies: real or bias? Diab Care. 2018;41:6\u201310.","journal-title":"Diab Care"},{"key":"302_CR7","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1016\/j.jclinepi.2013.05.012","volume":"66","author":"S Schneeweiss","year":"2013","unstructured":"Schneeweiss S, Seeger JD, Jackson JW, Smith SR. Methods for comparative effectiveness research\/patient-centered outcomes research: from efficacy to effectiveness. J Clin Epidemiol. 2013;66:S1\u20134.","journal-title":"J Clin Epidemiol"},{"key":"302_CR8","first-page":"665","volume":"3","author":"G Derosa","year":"2007","unstructured":"Derosa G, Sibilla S. Optimizing combination treatment in the management of type 2 diabetes. Vasc Health Risk Manag. 2007;3:665\u201371.","journal-title":"Vasc Health Risk Manag"},{"key":"302_CR9","doi-asserted-by":"publisher","first-page":"c4444","DOI":"10.1136\/bmj.c4444","volume":"341","author":"J Green","year":"2010","unstructured":"Green J, Czanner G, Reeves G, Watson J, Wise L, Beral V. Oral bisphosphonates and risk of cancer of oesophagus, stomach, and colorectum: case-control analysis within a UK primary care cohort. BMJ. 2010;341:c4444.","journal-title":"BMJ"},{"key":"302_CR10","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1001\/jama.2010.1098","volume":"304","author":"CR Cardwell","year":"2010","unstructured":"Cardwell CR, Abnet CC, Cantwell MM, Murray LJ. Exposure to oral bisphosphonates and risk of esophageal cancer. JAMA. 2010;304:657\u201363.","journal-title":"JAMA"},{"key":"302_CR11","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1056\/NEJMoa1608664","volume":"376","author":"A Rawshani","year":"2017","unstructured":"Rawshani A, Rawshani A, Franz\u00e9n S, Eliasson B, Svensson A-M, Miftaraj M, et al. Mortality and cardiovascular disease in type 1 and type 2 diabetes. N Engl J Med. 2017;376:1407\u201318.","journal-title":"N Engl J Med"},{"key":"302_CR12","doi-asserted-by":"publisher","first-page":"2215","DOI":"10.1056\/NEJMoa1806802","volume":"380","author":"PD Reaven","year":"2019","unstructured":"Reaven PD, Emanuele NV, Wiitala WL, Bahn GD, Reda DJ, McCarren M, et al. Intensive glucose control in patients with type 2 diabetes\u201415-year follow-up. N Engl J Med. 2019;380:2215\u201324.","journal-title":"N Engl J Med"},{"key":"302_CR13","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1214\/09-SS057","volume":"3","author":"J Pearl","year":"2009","unstructured":"Pearl J. Causal inference in statistics: an overview. Stat Surv. 2009;3:96\u2013146.","journal-title":"Stat Surv"},{"key":"302_CR14","volume-title":"Machine learning: a probabilistic perspective (adaptive computation and machine learning series)","author":"KP Murphy","year":"2012","unstructured":"Murphy KP. Machine learning: a probabilistic perspective (adaptive computation and machine learning series). USA: MIT Press; 2012."},{"key":"302_CR15","doi-asserted-by":"publisher","first-page":"267","DOI":"10.3399\/bjgp13X665233","volume":"63","author":"WM Wami","year":"2013","unstructured":"Wami WM, Buntinx F, Bartholomeeusen S, Goderis G, Mathieu C, Aerts M. Influence of chronic comorbidity and medication on the efficacy of treatment in patients with diabetes in general practice. Br J Gen Pr. 2013;63:267\u201373.","journal-title":"Br J Gen Pr"},{"key":"302_CR16","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1016\/j.ymgme.2015.12.001","volume":"117","author":"J Utz","year":"2016","unstructured":"Utz J, Whitley CB, van Giersbergen PL, Kolb SA. Comorbidities and pharmacotherapies in patients with Gaucher disease type 1: the potential for drug\u2013drug interactions. Mol Genet Metab. 2016;117:172\u20138.","journal-title":"Mol Genet Metab"},{"key":"302_CR17","doi-asserted-by":"publisher","first-page":"237","DOI":"10.1186\/s12879-018-3148-z","volume":"18","author":"DJ Ruzicka","year":"2018","unstructured":"Ruzicka DJ, Tetsuka J, Fujimoto G, Kanto T. Comorbidities and co-medications in populations with and without chronic hepatitis C virus infection in Japan between 2015 and 2016. BMC Infect Dis. 2018;18:237.","journal-title":"BMC Infect Dis"},{"key":"302_CR18","doi-asserted-by":"publisher","first-page":"139","DOI":"10.4103\/picr.PICR_81_17","volume":"9","author":"R Indu","year":"2018","unstructured":"Indu R, Adhikari A, Maisnam I, Basak P, Sur TK, Das AK. Polypharmacy and comorbidity status in the treatment of type 2 diabetic patients attending a tertiary care hospital: an observational and questionnaire-based study. Perspect Clin Res. 2018;9:139\u201344.","journal-title":"Perspect Clin Res"},{"key":"302_CR19","doi-asserted-by":"publisher","first-page":"79","DOI":"10.1007\/s40120-019-0131-6","volume":"8","author":"D Keine","year":"2019","unstructured":"Keine D, Zelek M, Walker JQ, Sabbagh MN. Polypharmacy in an elderly population: enhancing medication management through the use of clinical decision support software platforms. Neurol Ther. 2019;8:79\u201394.","journal-title":"Neurol Ther"},{"key":"302_CR20","doi-asserted-by":"publisher","first-page":"1850017","DOI":"10.1142\/S0219720018500178","volume":"16","author":"A Sharma","year":"2018","unstructured":"Sharma A, Rani R. An integrated framework for identification of effective and synergistic anti-cancer drug combinations. J Bioinform Comput Biol. 2018;16:1850017.","journal-title":"J Bioinform Comput Biol"},{"key":"302_CR21","doi-asserted-by":"publisher","first-page":"E208","DOI":"10.3390\/pharmaceutics11050208","volume":"11","author":"V Vakil","year":"2019","unstructured":"Vakil V, Trappe W. Drug combinations: mathematical modeling and networking methods. Pharmaceutics. 2019;11:E208.","journal-title":"Pharmaceutics"},{"key":"302_CR22","doi-asserted-by":"publisher","first-page":"71","DOI":"10.1186\/s12859-018-2509-3","volume":"19","author":"F Xia","year":"2018","unstructured":"Xia F, Shukla M, Brettin T, Garcia-Cardona C, Cohn J, Allen JE, et al. Predicting tumor cell line response to drug pairs with deep learning. BMC Bioinform. 2018;19:71\u20139.","journal-title":"BMC Bioinform"},{"key":"302_CR23","doi-asserted-by":"publisher","first-page":"1434","DOI":"10.1093\/bib\/bby004","volume":"20","author":"IF Tsigelny","year":"2019","unstructured":"Tsigelny IF. Artificial intelligence in drug combination therapy. Brief Bioinform. 2019;20:1434\u201348.","journal-title":"Brief Bioinform"},{"key":"302_CR24","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1089\/pop.2018.0129","volume":"22","author":"I Dankwa-Mullan","year":"2019","unstructured":"Dankwa-Mullan I, Rivo M, Sepulveda M, Park Y, Snowdon J, Rhee K. Transforming diabetes care through artificial intelligence: the future is here. Popul Health Manag. 2019;22:229\u201342.","journal-title":"Popul Health Manag"},{"key":"302_CR25","doi-asserted-by":"publisher","first-page":"e10775","DOI":"10.2196\/10775","volume":"20","author":"I Contreras","year":"2018","unstructured":"Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. 2018;20:e10775.","journal-title":"J Med Internet Res"},{"key":"302_CR26","doi-asserted-by":"publisher","first-page":"598","DOI":"10.1097\/MPA.0000000000001312","volume":"48","author":"A Bradley","year":"2019","unstructured":"Bradley A, Van Der Meer R, McKay C. Personalized pancreatic cancer management: a systematic review of how machine learning is supporting decision-making. Pancreas. 2019;48:598\u2013604.","journal-title":"Pancreas"},{"key":"302_CR27","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0213292","author":"X Jiang","year":"2019","unstructured":"Jiang X, Wells A, Brufsky A, Neapolitan R. A clinical decision support system learned from data to personalize treatment recommendations towards preventing breast cancer metastasis. PloS ONE. 2019. https:\/\/doi.org\/10.1371\/journal.pone.0213292.","journal-title":"PloS ONE"},{"key":"302_CR28","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1097\/MAT.0000000000000209","volume":"61","author":"NA Loghmanpour","year":"2015","unstructured":"Loghmanpour NA, Kanwar MK, Druzdzel MJ, Benza RL, Murali S, Antaki JF. A new Bayesian network-based risk stratification model for prediction of short-term and long-term LVAD mortality. ASAIO J. 2015;61:313\u201323.","journal-title":"ASAIO J"},{"key":"302_CR29","doi-asserted-by":"publisher","first-page":"172","DOI":"10.1007\/978-3-319-19551-3_22","volume-title":"Artif Intell Med","author":"F Sambo","year":"2015","unstructured":"Sambo F, Facchinetti A, Hakaste L, Kravic J, Di Camillo B, Fico G, et al. A Bayesian network for probabilistic reasoning and imputation of missing risk factors in type 2 diabetes. In: Holmes JH, Bellazzi R, Sacchi L, Peek N, editors. Artif Intell Med. Cham: Springer International Publishing; 2015. p. 172\u20136."},{"key":"302_CR30","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41598-017-07029-7","volume":"7","author":"S Chamaria","year":"2017","unstructured":"Chamaria S, Johnson KW, Vengrenyuk Y, Baber U, Shameer K, Divaraniya AA, et al. Intracoronary imaging, cholesterol efflux, and transcriptomics after intensive statin treatment in diabetes. Sci Rep. 2017;7:1\u201313.","journal-title":"Sci Rep"},{"key":"302_CR31","doi-asserted-by":"publisher","first-page":"284","DOI":"10.1007\/s12020-018-1740-z","volume":"63","author":"M Porta","year":"2019","unstructured":"Porta M, Amione C, Barutta F, Fornengo P, Merlo S, Gruden G, et al. The co-activator-associated arginine methyltransferase 1 (CARM1) gene is overexpressed in type 2 diabetes. Endocrine. 2019;63:284\u201392.","journal-title":"Endocrine"},{"key":"302_CR32","doi-asserted-by":"crossref","unstructured":"Kj\u00e6rulff UB, Madsen AL. Bayesian Networks and Influence Diagrams: A Guide to Construction and Analysis. 2nd ed. Springer; 2012.","DOI":"10.1007\/978-1-4614-5104-4"},{"key":"302_CR33","doi-asserted-by":"crossref","unstructured":"Pearl J. Causality: Models, Reasoning and Inference. 2nd ed. Cambridge University Press; 2009.","DOI":"10.1017\/CBO9780511803161"},{"key":"302_CR34","unstructured":"Korb KB, Nicholson AE. Bayesian artificial intelligence [Internet]. CRC press; 2010 [cited 2016 Jul 12]. Available from: https:\/\/books.google.ca\/books?hl=en&lr=&id=LxXOBQAAQBAJ&oi=fnd&pg=PP1&dq=Nicholson+AE.+Bayesian+artificial+intelligence&ots=Q1WG9bh2D5&sig=-khUK5bYwxirxpBBzRof-QmDUY0."},{"key":"302_CR35","doi-asserted-by":"publisher","first-page":"266","DOI":"10.1016\/j.compeleceng.2017.02.013","volume":"61","author":"N Milosevic","year":"2017","unstructured":"Milosevic N, Dehghantanha A, Choo KKR. Machine learning aided android malware classification. Comput Electr Eng. 2017;61:266\u201374.","journal-title":"Comput Electr Eng"},{"key":"302_CR36","doi-asserted-by":"publisher","first-page":"14277","DOI":"10.1109\/ACCESS.2018.2806420","volume":"6","author":"K Randhawa","year":"2018","unstructured":"Randhawa K, Loo CK, Seera M, Lim CP, Nandi AK. Credit card fraud detection using AdaBoost and majority voting. IEEE Access. 2018;6:14277\u201384.","journal-title":"IEEE Access"},{"key":"302_CR37","doi-asserted-by":"publisher","first-page":"2","DOI":"10.1186\/1478-7954-4-2","volume":"4","author":"M-W Sohn","year":"2006","unstructured":"Sohn M-W, Arnold N, Maynard C, Hynes DM. Accuracy and completeness of mortality data in the Department of Veterans Affairs. Popul Health Metr. 2006;4:2.","journal-title":"Popul Health Metr"},{"key":"302_CR38","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1097\/00019514-200604000-00002","volume":"15","author":"F Alemi","year":"2006","unstructured":"Alemi F, Walters SR. A mathematical theory for identifying and measuring severity of episodes of care. Qual Manag Health Care. 2006;15:72\u201382.","journal-title":"Qual Manag Health Care"},{"key":"302_CR39","doi-asserted-by":"crossref","first-page":"740","DOI":"10.18553\/jmcp.2013.19.9.740","volume":"19","author":"RE Kheirbek","year":"2013","unstructured":"Kheirbek RE, Alemi F, Zargoush M. Comparative effectiveness of hypoglycemic medications among veterans. J Manag Care Pharm. 2013;19:740\u20134.","journal-title":"J Manag Care Pharm"},{"key":"302_CR40","doi-asserted-by":"crossref","unstructured":"Husmeier D, Dybowski R, Roberts S. Probabilistic modeling in bioinformatics and medical informatics. Springer Science & Business Media; 2006.","DOI":"10.1007\/b138794"},{"key":"302_CR41","volume-title":"Pattern recognition and neural networks","author":"BD Ripley","year":"2008","unstructured":"Ripley BD. Pattern recognition and neural networks. New york: Cambridge University Press; 2008."},{"key":"302_CR42","unstructured":"Friedman N, Nachman I, Pe\u2019er D. Learning Bayesian network structure from massive datasets: the \u201csparse candidate\u201d algorithm. arXiv:13016696. 2013."},{"key":"302_CR43","volume-title":"Bayesian networks and bayesiaLab: a practical introduction for researchers","author":"S Conrady","year":"2015","unstructured":"Conrady S, Jouffe L. Bayesian networks and bayesiaLab: a practical introduction for researchers. USA: Bayesia; 2015."},{"key":"302_CR44","doi-asserted-by":"publisher","DOI":"10.1201\/b21982","volume-title":"Risk Assessment and decision analysis with Bayesian networks","author":"N Fenton","year":"2018","unstructured":"Fenton N, Neil M. Risk Assessment and decision analysis with Bayesian networks. Boca Raton: Chapman and Hall\/CRC; 2018."},{"key":"302_CR45","first-page":"1","volume":"369","author":"AN Dey","year":"2006","unstructured":"Dey AN, Lucas JW. Physical and mental health characteristics of US-and foreign-born adults: United States, 1998\u20132003. Adv Data. 2006;369:1\u201319.","journal-title":"Adv Data"},{"key":"302_CR46","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.2105\/AJPH.2011.300616","volume":"102","author":"CJ Caspersen","year":"2012","unstructured":"Caspersen CJ, Thomas GD, Boseman LA, Beckles GL, Albright AL. Aging, diabetes, and the public health system in the United States. Am J Public Health. 2012;102:1482\u201397.","journal-title":"Am J Public Health"},{"key":"302_CR47","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.socscimed.2006.08.031","volume":"64","author":"L Manzoli","year":"2007","unstructured":"Manzoli L, Villari P, Pirone GM, Boccia A. Marital status and mortality in the elderly: a systematic review and meta-analysis. Soc Sci Med. 2007;64:77\u201394.","journal-title":"Soc Sci Med"},{"key":"302_CR48","doi-asserted-by":"publisher","first-page":"1585","DOI":"10.1161\/01.CIR.0000012546.20194.33","volume":"105","author":"JK Ghali","year":"2002","unstructured":"Ghali JK, Pi\u00f1a IL, Gottlieb SS, Deedwania PC, Wikstrand JC. Metoprolol CR\/XL in female patients with heart failure analysis of the experience in metoprolol extended-release randomized intervention trial in heart failure (MERIT-HF). Circulation. 2002;105:1585\u201391.","journal-title":"Circulation"},{"key":"302_CR49","doi-asserted-by":"publisher","first-page":"129","DOI":"10.1016\/j.ehj.2003.10.003","volume":"25","author":"F Gustafsson","year":"2004","unstructured":"Gustafsson F, Torp-Pedersen C, Burchardt H, Buch P, Seibaek M, Kj\u00f8ller E, et al. Female sex is associated with a better long-term survival in patients hospitalized with congestive heart failure. Eur Heart J. 2004;25:129\u201335.","journal-title":"Eur Heart J"},{"key":"302_CR50","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1161\/01.CIR.103.3.375","volume":"103","author":"T Simon","year":"2001","unstructured":"Simon T, Mary-Krause M, Funck-Brentano C, Jaillon P. Sex differences in the prognosis of congestive heart failure: results from the Cardiac Insufficiency Bisoprolol Study (CIBIS II). Circulation. 2001;103:375\u201380.","journal-title":"Circulation"},{"key":"302_CR51","doi-asserted-by":"publisher","first-page":"1147","DOI":"10.1016\/j.amjcard.2004.07.081","volume":"94","author":"WY Lee","year":"2004","unstructured":"Lee WY, Capra AM, Jensvold NG, Gurwitz JH, Go AS. Gender and risk of adverse outcomes in heart failure. Am J Cardiol. 2004;94:1147\u201352.","journal-title":"Am J Cardiol"},{"key":"302_CR52","doi-asserted-by":"publisher","first-page":"353","DOI":"10.1016\/S0002-9149(00)00934-6","volume":"86","author":"C Opasich","year":"2000","unstructured":"Opasich C, Tavazzi L, Lucci D, Gorini M, Albanese MC, Cacciatore G, et al. Comparison of one-year outcome in women versus men with chronic congestive heart failure. Am J Cardiol. 2000;86:353\u20137.","journal-title":"Am J Cardiol"},{"key":"302_CR53","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1016\/j.amjcard.2004.12.033","volume":"95","author":"R Sheppard","year":"2005","unstructured":"Sheppard R, Behlouli H, Richard H, Pilote L. Effect of gender on treatment, resource utilization, and outcomes in congestive heart failure in Quebec, Canada. Am J Cardiol. 2005;95:955\u20139.","journal-title":"Am J Cardiol"},{"key":"302_CR54","doi-asserted-by":"publisher","first-page":"1900","DOI":"10.1093\/eurheartj\/ehr077","volume":"32","author":"TK Schramm","year":"2011","unstructured":"Schramm TK, Gislason GH, Vaag A, Rasmussen JN, Folke F, Hansen ML, et al. Mortality and cardiovascular risk associated with different insulin secretagogues compared with metformin in type 2 diabetes, with or without a previous myocardial infarction: a nationwide study. Eur Heart J. 2011;32:1900\u20138.","journal-title":"Eur Heart J"},{"key":"302_CR55","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.1001\/archinternmed.2010.207","volume":"170","author":"SE Nissen","year":"2010","unstructured":"Nissen SE, Wolski K. Rosiglitazone revisited: an updated meta-analysis of risk for myocardial infarction and cardiovascular mortality. Arch Intern Med. 2010;170:1191\u2013201.","journal-title":"Arch Intern Med"},{"key":"302_CR56","doi-asserted-by":"publisher","first-page":"347","DOI":"10.1517\/14740331003623218","volume":"9","author":"P Shah","year":"2010","unstructured":"Shah P, Mudaliar S. Pioglitazone: side effect and safety profile. Expert Opin Drug Saf. 2010;9:347\u201354.","journal-title":"Expert Opin Drug Saf"},{"key":"302_CR57","doi-asserted-by":"publisher","first-page":"1224","DOI":"10.2337\/dc10-0017","volume":"33","author":"KM Pantalone","year":"2010","unstructured":"Pantalone KM, Kattan MW, Yu C, Wells BJ, Arrigain S, Jain A, et al. The risk of overall mortality in patients with type 2 diabetes receiving glipizide, glyburide, or glimepiride monotherapy a retrospective analysis. Diab Care. 2010;33:1224\u20139.","journal-title":"Diab Care"},{"key":"302_CR58","doi-asserted-by":"publisher","first-page":"1029","DOI":"10.1111\/j.1464-5491.2012.03577.x","volume":"29","author":"KM Pantalone","year":"2012","unstructured":"Pantalone KM, Kattan MW, Yu C, Wells BJ, Arrigain S, Nutter B, et al. The risk of overall mortality in patients with type 2 diabetes receiving different combinations of sulfonylureas and metformin: a retrospective analysis. Diab Med. 2012;29:1029\u201335.","journal-title":"Diab Med"},{"issue":"2011","key":"302_CR59","first-page":"898913","volume":"2011","author":"I Dicembrini","year":"2011","unstructured":"Dicembrini I, Pala L, Rotella CM. From theory to clinical practice in the use of GLP-1 receptor agonists and DPP-4 inhibitors therapy. Exp Diab Res. 2011;2011(2011):898913.","journal-title":"Exp Diab Res"},{"key":"302_CR60","doi-asserted-by":"publisher","first-page":"5","DOI":"10.2337\/cd16-0067","volume":"35","author":"American Diabetes Association","year":"2017","unstructured":"American Diabetes Association. Standards of medical care in diabetes\u20142017 abridged for primary care providers. Clin Diab. 2017;35:5\u201326.","journal-title":"Clin Diab"},{"key":"302_CR61","doi-asserted-by":"publisher","DOI":"10.1155\/2019\/7676909","author":"AN Mekuria","year":"2019","unstructured":"Mekuria AN, Ayele Y, Tola A, Mishore KM. Monotherapy with metformin versus sulfonylureas and risk of cancer in type 2 diabetic patients: a systematic review and meta-analysis. J Diab Res. 2019. https:\/\/doi.org\/10.1155\/2019\/7676909.","journal-title":"J Diab Res"},{"key":"302_CR62","doi-asserted-by":"publisher","first-page":"960","DOI":"10.1002\/asi.10132","volume":"53","author":"B Hj\u00f8rland","year":"2002","unstructured":"Hj\u00f8rland B, Christensen FS. Work tasks and socio-cognitive relevance: a specific example. J Am Soc Inf Sci Technol. 2002;53:960\u20135.","journal-title":"J Am Soc Inf Sci Technol"},{"key":"302_CR63","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1287\/ijoc.1070.0255","volume":"20","author":"X Bai","year":"2008","unstructured":"Bai X, Padman R, Ramsey J, Spirtes P. Tabu search-enhanced graphical models for classification in high dimensions. Inf J Comput. 2008;20:423\u201337.","journal-title":"Inf J Comput"},{"key":"302_CR64","doi-asserted-by":"publisher","first-page":"2291","DOI":"10.1111\/poms.12872","volume":"27","author":"M Zargoush","year":"2018","unstructured":"Zargoush M, Gumus M, Verter V, Daskalopoulou SS. Designing risk-adjusted therapy for patients with hypertension. Prod Oper Manag. 2018;27:2291\u2013312.","journal-title":"Prod Oper Manag"},{"key":"302_CR65","doi-asserted-by":"publisher","first-page":"1339","DOI":"10.1287\/mnsc.2015.2180","volume":"62","author":"T Ayer","year":"2015","unstructured":"Ayer T, Alagoz O, Stout NK, Burnside ES. Heterogeneity in women\u2019s adherence and its role in optimal breast cancer screening policies. Manag Sci. 2015;62:1339\u201362.","journal-title":"Manag Sci"}],"container-title":["Journal of Big Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00302-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s40537-020-00302-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s40537-020-00302-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,9]],"date-time":"2021-04-09T23:06:31Z","timestamp":1618009591000},"score":1,"resource":{"primary":{"URL":"https:\/\/journalofbigdata.springeropen.com\/articles\/10.1186\/s40537-020-00302-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,4,10]]},"references-count":65,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2020,12]]}},"alternative-id":["302"],"URL":"https:\/\/doi.org\/10.1186\/s40537-020-00302-z","relation":{},"ISSN":["2196-1115"],"issn-type":[{"value":"2196-1115","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,4,10]]},"assertion":[{"value":"15 November 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"30 March 2020","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"10 April 2020","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"Not applicable.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"The authors grant the sole license of the full copyright in the contribution and guarantee that the contribution to the work has not been previously published elsewhere.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"26"}}