{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T07:19:32Z","timestamp":1740122372484,"version":"3.37.3"},"reference-count":24,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T00:00:00Z","timestamp":1620000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Data Min Knowl Disc"],"published-print":{"date-parts":[[2021,7]]},"DOI":"10.1007\/s10618-021-00758-4","type":"journal-article","created":{"date-parts":[[2021,5,3]],"date-time":"2021-05-03T11:02:57Z","timestamp":1620039777000},"page":"1739-1759","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Affinity analysis for studying physicians\u2019 prescription behavior."],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0876-8167","authenticated-orcid":false,"given":"Iraklis","family":"Varlamis","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,3]]},"reference":[{"key":"758_CR1","doi-asserted-by":"publisher","first-page":"207","DOI":"10.1145\/170036.170072","volume":"22","author":"R Agrawal","year":"1993","unstructured":"Agrawal R, Imieli\u0144ski T, Swami A (1993) Mining association rules between sets of items in large databases. ACM Sigmod Record 22:207\u2013216","journal-title":"ACM Sigmod Record"},{"issue":"6","key":"758_CR2","doi-asserted-by":"publisher","first-page":"885","DOI":"10.1007\/BF02850210","volume":"23","author":"B Akdag","year":"2006","unstructured":"Akdag B, Fenkci S, Degirmencioglu S, Rota S, Sermez Y, Camdeviren H (2006) Determination of risk factors for hypertension through the classification tree method. Adv Ther 23(6):885\u2013892","journal-title":"Adv Ther"},{"issue":"3","key":"758_CR3","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1023\/A:1011429418057","volume":"5","author":"SD Bay","year":"2001","unstructured":"Bay SD, Pazzani MJ (2001) Detecting group differences: mining contrast sets. Data Min Knowl Discov 5(3):213\u2013246","journal-title":"Data Min Knowl Discov"},{"key":"758_CR4","doi-asserted-by":"crossref","unstructured":"Bhatnagar D, Soran H, Durrington PN (2008) Hypercholesterolaemia and its management. Br Med J 337","DOI":"10.1136\/bmj.a993"},{"issue":"1","key":"758_CR5","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1097\/PSY.0b013e3181c4fca1","volume":"72","author":"D Carroll","year":"2010","unstructured":"Carroll D, Phillips AC, Gale CR, Batty GD (2010) Generalized anxiety and major depressive disorders, their comorbidity and hypertension in middle-aged men. Psychos Med 72(1):16\u201319","journal-title":"Psychos Med"},{"issue":"5","key":"758_CR6","doi-asserted-by":"publisher","first-page":"5507","DOI":"10.1016\/j.eswa.2010.10.086","volume":"38","author":"CD Chang","year":"2011","unstructured":"Chang CD, Wang CC, Jiang BC (2011) Using data mining techniques for multi-diseases prediction modeling of hypertension and hyperlipidemia by common risk factors. Expert Syst Appl 38(5):5507\u20135513","journal-title":"Expert Syst Appl"},{"key":"758_CR7","doi-asserted-by":"crossref","unstructured":"Dharshinni N, Mawengkang H, Nasution M (2018) Mapping of medicine data with k-means and apriori combinations based on patient diagnosis. In: 2nd International conference on computing and applied informatics, J. Phys.: Conf. Series, 978 IOP Publishing","DOI":"10.1088\/1742-6596\/978\/1\/012027"},{"issue":"3","key":"758_CR8","first-page":"193","volume":"13","author":"S Dogan","year":"2008","unstructured":"Dogan S, Turkoglu I (2008) Diagnosing hyperlipidemia using association rules. Math Comput Appl 13(3):193\u2013202","journal-title":"Math Comput Appl"},{"key":"758_CR9","doi-asserted-by":"publisher","first-page":"63","DOI":"10.1007\/978-3-030-31463-7_5","volume-title":"Challenges in social network research","author":"G Giordano","year":"2020","unstructured":"Giordano G, De Santis M, Pagano S, Ragozini G, Vitale MP, Cavallo P (2020) Association rules and network analysis for exploring comorbidity patterns in health systems. Challenges in social network research. Springer, Berlin, pp 63\u201378"},{"issue":"8","key":"758_CR10","doi-asserted-by":"publisher","first-page":"955","DOI":"10.1089\/thy.2012.0626","volume":"23","author":"T Ittermann","year":"2013","unstructured":"Ittermann T, Tiller D, Meisinger C, Agger C, Nauck M, Rettig R, Hofman A, J\u00f8rgensen T, Linneberg A, Witteman JC et al (2013) High serum thyrotropin levels are associated with current but not with incident hypertension. Thyroid 23(8):955\u2013963","journal-title":"Thyroid"},{"key":"758_CR11","unstructured":"Jiawei\u00a0Han M, Pei J (2011) Data mining: concepts and techniques: concepts and techniques"},{"issue":"12","key":"758_CR12","doi-asserted-by":"publisher","first-page":"1209","DOI":"10.1016\/j.jacc.2009.10.053","volume":"55","author":"KK Koh","year":"2010","unstructured":"Koh KK, Quon MJ, Han SH, Lee Y, Kim SJ, Shin EK (2010) Atorvastatin causes insulin resistance and increases ambient glycemia in hypercholesterolemic patients. J Am Coll Cardiol 55(12):1209\u20131216","journal-title":"J Am Coll Cardiol"},{"issue":"3","key":"758_CR13","doi-asserted-by":"publisher","first-page":"284","DOI":"10.5056\/jnm.2012.18.3.284","volume":"18","author":"SP Lee","year":"2012","unstructured":"Lee SP, Lee KN, Lee OY, Lee HL, Choi HS, Yoon BC, Jun DW, Sohn W, Cho SC (2012) The relationship between existence of typical symptoms and psychological factors in patients with erosive esophagitis. J Neurogastroenterol Motil 18(3):284","journal-title":"J Neurogastroenterol Motil"},{"key":"758_CR14","first-page":"377","volume":"10","author":"PK Novak","year":"2009","unstructured":"Novak PK, Lavra\u010d N, Webb GI (2009) Supervised descriptive rule discovery: a unifying survey of contrast set, emerging pattern and subgroup mining. J Mach Learn Res 10:377\u2013403","journal-title":"J Mach Learn Res"},{"issue":"1","key":"758_CR15","doi-asserted-by":"publisher","first-page":"84","DOI":"10.1016\/j.cmpb.2013.07.004","volume":"112","author":"C Ou-Yang","year":"2013","unstructured":"Ou-Yang C, Agustianty S, Wang HC (2013) Developing a data mining approach to investigate association between physician prescription and patient outcome-a study on re-hospitalization in stevens-johnson syndrome. Comput Methods Programs Biomed 112(1):84\u201391","journal-title":"Comput Methods Programs Biomed"},{"issue":"1","key":"758_CR16","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1186\/s12911-017-0496-3","volume":"17","author":"O Pattanaprateep","year":"2017","unstructured":"Pattanaprateep O, McEvoy M, Attia J, Thakkinstian A (2017) Evaluation of rational nonsteroidal anti-inflammatory drugs and gastro-protective agents use; association rule data mining using outpatient prescription patterns. BMC Med Informat Decis Making 17(1):96","journal-title":"BMC Med Informat Decis Making"},{"issue":"5","key":"758_CR17","doi-asserted-by":"publisher","first-page":"575","DOI":"10.1016\/j.cjca.2017.12.005","volume":"34","author":"JR Petrie","year":"2018","unstructured":"Petrie JR, Guzik TJ, Touyz RM (2018) Diabetes, hypertension, and cardiovascular disease: clinical insights and vascular mechanisms. Canadian J Cardiol 34(5):575\u2013584","journal-title":"Canadian J Cardiol"},{"key":"758_CR18","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1007\/978-3-319-19156-0_6","volume-title":"Int Conf Health Inf Sci","author":"J Reps","year":"2015","unstructured":"Reps J, Guo Z, Zhu H, Aickelin U (2015) Identifying candidate risk factors for prescription drug side effects using causal contrast set mining. Int Conf Health Inf Sci. Springer, Berlin, pp 45\u201355"},{"key":"758_CR19","doi-asserted-by":"crossref","unstructured":"Sanida T, Varlamis I (2017) Application of affinity analysis techniques on diagnosis and prescription data. In: 2017 IEEE 30th International symposium on computer-based medical systems (CBMS), IEEE, pp 403\u2013408","DOI":"10.1109\/CBMS.2017.114"},{"issue":"3","key":"758_CR20","doi-asserted-by":"publisher","first-page":"583","DOI":"10.1016\/j.eswa.2005.04.014","volume":"29","author":"M Ture","year":"2005","unstructured":"Ture M, Kurt I, Kurum AT, Ozdamar K (2005) Comparing classification techniques for predicting essential hypertension. Expert Syst Appl 29(3):583\u2013588","journal-title":"Expert Syst Appl"},{"issue":"14","key":"758_CR21","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.2165\/00003495-200666140-00011","volume":"66","author":"AJ Wagstaff","year":"2006","unstructured":"Wagstaff AJ (2006) Valsartan\/hydrochlorothiazide. Drugs 66(14):1881\u20131901","journal-title":"Drugs"},{"key":"758_CR22","unstructured":"World Health Organization (2007) UNAIDS. prevention of cardiovascular disease. World Health Organization"},{"key":"758_CR23","doi-asserted-by":"publisher","first-page":"73","DOI":"10.1016\/j.jbi.2014.09.003","volume":"53","author":"AP Wright","year":"2015","unstructured":"Wright AP, Wright AT, McCoy AB, Sittig DF (2015) The use of sequential pattern mining to predict next prescribed medications. J Biomed Informat 53:73\u201380","journal-title":"J Biomed Informat"},{"issue":"12","key":"758_CR24","doi-asserted-by":"publisher","first-page":"1469","DOI":"10.1016\/j.metabol.2014.08.010","volume":"63","author":"L Wu","year":"2014","unstructured":"Wu L, Parhofer KG (2014) Diabetic dyslipidemia. Metabolism 63(12):1469\u20131479","journal-title":"Metabolism"}],"container-title":["Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00758-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10618-021-00758-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10618-021-00758-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,6,23]],"date-time":"2021-06-23T08:03:14Z","timestamp":1624435394000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10618-021-00758-4"}},"subtitle":["The case of hypertension and hyperlipidemia"],"short-title":[],"issued":{"date-parts":[[2021,5,3]]},"references-count":24,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2021,7]]}},"alternative-id":["758"],"URL":"https:\/\/doi.org\/10.1007\/s10618-021-00758-4","relation":{},"ISSN":["1384-5810","1573-756X"],"issn-type":[{"type":"print","value":"1384-5810"},{"type":"electronic","value":"1573-756X"}],"subject":[],"published":{"date-parts":[[2021,5,3]]},"assertion":[{"value":"20 December 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 April 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 May 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}