{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T15:57:06Z","timestamp":1775145426003,"version":"3.50.1"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Health Inf Sci Syst"],"published-print":{"date-parts":[[2016,12]]},"DOI":"10.1186\/s13755-016-0015-4","type":"journal-article","created":{"date-parts":[[2016,3,7]],"date-time":"2016-03-07T20:42:29Z","timestamp":1457383349000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":84,"title":["Automatically explaining machine learning prediction results: a demonstration on type 2 diabetes risk prediction"],"prefix":"10.1007","volume":"4","author":[{"given":"Gang","family":"Luo","sequence":"first","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2016,3,8]]},"reference":[{"key":"15_CR1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-77244-8","volume-title":"Clinical prediction models: a practical approach to development, validation, and updating","author":"EW Steyerberg","year":"2009","unstructured":"Steyerberg EW. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer; 2009."},{"issue":"4","key":"15_CR2","doi-asserted-by":"publisher","first-page":"e128","DOI":"10.2196\/resprot.5039","volume":"4","author":"G Luo","year":"2015","unstructured":"Luo G, Stone BL, Sakaguchi F, Sheng X, Murtaugh MA. Using computational approaches to improve risk-stratified patient management: rationale and methods. JMIR Res Protoc. 2015;4(4):e128.","journal-title":"JMIR Res Protoc"},{"issue":"5","key":"15_CR3","doi-asserted-by":"publisher","first-page":"1040","DOI":"10.2337\/dc07-1150","volume":"31","author":"KE Heikes","year":"2008","unstructured":"Heikes KE, Eddy DM, Arondekar B, Schlessinger L. Diabetes Risk Calculator: a simple tool for detecting undiagnosed diabetes and pre-diabetes. Diabetes Care. 2008;31(5):1040\u20135.","journal-title":"Diabetes Care"},{"issue":"3","key":"15_CR4","doi-asserted-by":"publisher","first-page":"725","DOI":"10.2337\/diacare.26.3.725","volume":"26","author":"J Lindstr\u00f6m","year":"2003","unstructured":"Lindstr\u00f6m J, Tuomilehto J. The diabetes risk score: a practical tool to predict type 2 diabetes risk. Diabetes Care. 2003;26(3):725\u201331.","journal-title":"Diabetes Care"},{"issue":"8","key":"15_CR5","doi-asserted-by":"publisher","first-page":"830","DOI":"10.1111\/j.1464-5491.2007.02173.x","volume":"24","author":"RK Simmons","year":"2007","unstructured":"Simmons RK, Harding AH, Wareham NJ, Griffin SJ. Do simple questions about diet and physical activity help to identify those at risk of type 2 diabetes? Diabet Med. 2007;24(8):830\u20135.","journal-title":"Diabet Med"},{"issue":"8","key":"15_CR6","doi-asserted-by":"publisher","first-page":"575","DOI":"10.7326\/0003-4819-136-8-200204160-00006","volume":"136","author":"MP Stern","year":"2002","unstructured":"Stern MP, Williams K, Haffner SM. Identification of persons at high risk for type 2 diabetes mellitus: do we need the oral glucose tolerance test? Ann Intern Med. 2002;136(8):575\u201381.","journal-title":"Ann Intern Med"},{"issue":"2","key":"15_CR7","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1109\/JBHI.2014.2325615","volume":"19","author":"L Han","year":"2015","unstructured":"Han L, Luo S, Yu J, Pan L, Chen S. Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J Biomed Health Inform. 2015;19(2):728\u201334.","journal-title":"IEEE J Biomed Health Inform"},{"key":"15_CR8","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6849-3","volume-title":"Applied predictive modeling","author":"M Kuhn","year":"2013","unstructured":"Kuhn M, Johnson K. Applied predictive modeling. New York: Springer; 2013."},{"issue":"12","key":"15_CR9","doi-asserted-by":"publisher","first-page":"779","DOI":"10.2165\/00115677-200311120-00003","volume":"11","author":"RC Axelrod","year":"2003","unstructured":"Axelrod RC, Vogel D. Predictive modeling in health plans. Dis Manag Health Outcomes. 2003;11(12):779\u201387.","journal-title":"Dis Manag Health Outcomes"},{"issue":"2","key":"15_CR10","doi-asserted-by":"publisher","first-page":"e88225","DOI":"10.1371\/journal.pone.0088225","volume":"9","author":"H Asadi","year":"2014","unstructured":"Asadi H, Dowling R, Yan B, Mitchell P. Machine learning for outcome prediction of acute ischemic stroke post intra-arterial therapy. PLoS ONE. 2014;9(2):e88225.","journal-title":"PLoS ONE"},{"issue":"1","key":"15_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/2594473.2594475","volume":"15","author":"AA Freitas","year":"2013","unstructured":"Freitas AA. Comprehensible classification models: a position paper. SIGKDD Explorations. 2013;15(1):1\u201310.","journal-title":"SIGKDD Explorations"},{"key":"15_CR12","first-page":"163","volume":"12","author":"A Vellido","year":"2012","unstructured":"Vellido A, Mart\u00ednGuerrero JD, Lisboa PJG. Making machine learning models interpretable. Proc ESANN. 2012;12:163\u201372.","journal-title":"Proc ESANN"},{"key":"15_CR13","unstructured":"Centers for Disease Control and Prevention. National diabetes statistics report. 2014. http:\/\/www.cdc.gov\/diabetes\/pubs\/statsreport14\/national-diabetes-report-web.pdf. Accessed 1 Jan 2016."},{"issue":"3","key":"15_CR14","doi-asserted-by":"publisher","first-page":"253","DOI":"10.1016\/j.amepre.2013.04.017","volume":"45","author":"X Zhuo","year":"2013","unstructured":"Zhuo X, Zhang P, Hoerger TJ. Lifetime direct medical costs of treating type 2 diabetes and diabetic complications. Am J Prev Med. 2013;45(3):253\u201361.","journal-title":"Am J Prev Med"},{"issue":"4","key":"15_CR15","doi-asserted-by":"publisher","first-page":"537","DOI":"10.2337\/diacare.20.4.537","volume":"20","author":"XR Pan","year":"1997","unstructured":"Pan XR, Li GW, Hu YH, Wang JX, Yang WY, An ZX, et al. Effects of diet and exercise in preventing NIDDM in people with impaired glucose tolerance. The Da Qing IGT and Diabetes Study. Diabetes Care. 1997;20(4):537\u201344.","journal-title":"Diabetes Care"},{"issue":"18","key":"15_CR16","doi-asserted-by":"publisher","first-page":"1343","DOI":"10.1056\/NEJM200105033441801","volume":"344","author":"J Tuomilehto","year":"2001","unstructured":"Tuomilehto J, Lindstr\u00f6m J, Eriksson JG, Valle TT, H\u00e4m\u00e4l\u00e4inen H, Ilanne-Parikka P, et al. Prevention of type 2 diabetes mellitus by changes in lifestyle among subjects with impaired glucose tolerance. N Engl J Med. 2001;344(18):1343\u201350.","journal-title":"N Engl J Med"},{"issue":"6","key":"15_CR17","doi-asserted-by":"publisher","first-page":"393","DOI":"10.1056\/NEJMoa012512","volume":"346","author":"WC Knowler","year":"2002","unstructured":"Knowler WC, Barrett-Connor E, Fowler SE, Hamman RF, Lachin JM, Walker EA, et al. Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin. N Engl J Med. 2002;346(6):393\u2013403.","journal-title":"N Engl J Med"},{"issue":"9323","key":"15_CR18","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.1016\/S0140-6736(02)08905-5","volume":"359","author":"JL Chiasson","year":"2002","unstructured":"Chiasson JL, Josse RG, Gomis R, Hanefeld M, Karasik A, Laakso M. Acarbose for prevention of type 2 diabetes mellitus: the STOP-NIDDM randomised trial. Lancet. 2002;359(9323):2072\u20137.","journal-title":"Lancet"},{"key":"15_CR19","unstructured":"Liu B, Hsu W, Ma Y. Integrating classification and association rule mining. Proc KDD. 1998:80\u20136."},{"key":"15_CR20","unstructured":"Li W, Han J, Pei J. CMAR: accurate and efficient classification based on multiple class-association rules. Proc ICDM. 2001: 369\u201376."},{"key":"15_CR21","doi-asserted-by":"crossref","unstructured":"Yin X, Han J. CPAR: classification based on predictive association rules. Proc SDM. 2003: 331\u20135.","DOI":"10.1137\/1.9781611972733.40"},{"issue":"1","key":"15_CR22","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1017\/S0269888907001026","volume":"22","author":"FA Thabtah","year":"2007","unstructured":"Thabtah FA. A review of associative classification mining. Knowl Eng. Rev. 2007;22(1):37\u201365.","journal-title":"Knowl Eng. Rev"},{"key":"15_CR23","unstructured":"Fayyad UM, Irani KB. Multi-interval discretization of continuous-valued attributes for classification learning. Proc IJCAI. 1993:1022\u20139."},{"issue":"3","key":"15_CR24","doi-asserted-by":"publisher","first-page":"215","DOI":"10.1016\/S0933-3657(00)00110-X","volume":"22","author":"G Richards","year":"2001","unstructured":"Richards G, Rayward-Smith VJ, S\u00f6nksen PH, Carey S, Weng C. Data mining for indicators of early mortality in a database of clinical records. Artif Intell Med. 2001;22(3):215\u201331.","journal-title":"Artif Intell Med"},{"issue":"5","key":"15_CR25","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1055\/s-0038-1634196","volume":"40","author":"MJ Pazzani","year":"2001","unstructured":"Pazzani MJ, Mani S, Shankle WR. Acceptance of rules generated by machine learning among medical experts. Methods Inf Med. 2001;40(5):380\u20135.","journal-title":"Methods Inf Med"},{"issue":"2","key":"15_CR26","doi-asserted-by":"publisher","first-page":"383","DOI":"10.1007\/s10916-010-9483-2","volume":"36","author":"G Luo","year":"2012","unstructured":"Luo G, Thomas SB, Tang C. Automatic home medical product recommendation. J Med Syst. 2012;36(2):383\u201398.","journal-title":"J Med Syst"},{"key":"15_CR27","doi-asserted-by":"crossref","unstructured":"Luo G, Tang C, Yang H, Wei X. Med Search: a specialized search engine for medical information retrieval. Proc CIKM. 2008: 143\u201352.","DOI":"10.1145\/1458082.1458104"},{"issue":"1","key":"15_CR28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1561\/1500000040","volume":"9","author":"RLT Santos","year":"2015","unstructured":"Santos RLT, Macdonald C, Ounis I. Search result diversification. Foundations and Trends in Inf Retrieval. 2015;9(1):1\u201390.","journal-title":"Foundations and Trends in Inf Retrieval"},{"key":"15_CR29","unstructured":"Practice Fusion diabetes classification homepage. 2016. https:\/\/www.kaggle.com\/c\/pf2012-diabetes."},{"key":"15_CR30","unstructured":"Youden\u2019s J statistic. 2016. https:\/\/en.wikipedia.org\/wiki\/Youden%27s_J_statistic."}],"container-title":["Health Information Science and Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s13755-016-0015-4","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,9,5]],"date-time":"2019-09-05T05:15:58Z","timestamp":1567660558000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1186\/s13755-016-0015-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2016,3,8]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2016,12]]}},"alternative-id":["15"],"URL":"https:\/\/doi.org\/10.1186\/s13755-016-0015-4","relation":{"has-review":[{"id-type":"doi","id":"10.3410\/f.726375110.793518768","asserted-by":"object"}]},"ISSN":["2047-2501"],"issn-type":[{"value":"2047-2501","type":"electronic"}],"subject":[],"published":{"date-parts":[[2016,3,8]]},"article-number":"2"}}