{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T20:22:33Z","timestamp":1743020553975,"version":"3.40.3"},"publisher-location":"Cham","reference-count":21,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030191559"},{"type":"electronic","value":"9783030191566"}],"license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2019]]},"DOI":"10.1007\/978-3-030-19156-6_17","type":"book-chapter","created":{"date-parts":[[2019,5,6]],"date-time":"2019-05-06T12:41:31Z","timestamp":1557146491000},"page":"177-186","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Research on Diabetes Management Strategy Based on Deep Belief Network"],"prefix":"10.1007","author":[{"given":"Yang","family":"Liu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhijie","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiaying","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ang","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jialin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,5,7]]},"reference":[{"key":"17_CR1","unstructured":"International Diabetes Federation (IDF) Diabetes Atlas Eighth Edition poster Homepage. \n                    http:\/\/diabetesatlas.org\/resources\/2017-atlas.html\n                    \n                  . Accessed 24 Sept 2018"},{"key":"17_CR2","doi-asserted-by":"publisher","first-page":"45","DOI":"10.1016\/j.procs.2015.03.182","volume":"47","author":"JP Kandhasamy","year":"2015","unstructured":"Kandhasamy, J.P., Balamurali, S.: Performance analysis of classifier models to predict diabetes mellitus. Proc. Comput. Sci. 47, 45\u201351 (2015)","journal-title":"Proc. Comput. Sci."},{"key":"17_CR3","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.procs.2015.10.014","volume":"69","author":"N Nai-Arun","year":"2015","unstructured":"Nai-Arun, N., Moungmai, R.: Comparison of classifiers for the risk of diabetes prediction. Proc. Comput. Sci. 69, 132\u2013142 (2015)","journal-title":"Proc. Comput. Sci."},{"issue":"6","key":"17_CR4","first-page":"82","volume":"55","author":"P Su","year":"2017","unstructured":"Su, P., Yang, Y., Yang, Y., et al.: Prediction models on the onset risks of type 2 diabetes among the health management population. J. Shandong Univ. (Health Sci.) 55(6), 82\u201386 (2017)","journal-title":"J. Shandong Univ. (Health Sci.)"},{"issue":"9","key":"17_CR5","doi-asserted-by":"publisher","first-page":"4265","DOI":"10.1016\/j.eswa.2015.01.042","volume":"42","author":"S Kang","year":"2015","unstructured":"Kang, S., Kang, P., Ko, T., et al.: An efficient and effective ensemble of support vector machines for anti-diabetic drug failure prediction. Expert Syst. Appl. 42(9), 4265\u20134273 (2015)","journal-title":"Expert Syst. Appl."},{"issue":"7","key":"17_CR6","doi-asserted-by":"publisher","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","volume":"18","author":"GE Hinton","year":"2006","unstructured":"Hinton, G.E., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18(7), 1527\u20131554 (2006)","journal-title":"Neural Comput."},{"issue":"3","key":"17_CR7","doi-asserted-by":"publisher","first-page":"257","DOI":"10.1016\/j.ymeth.2014.06.011","volume":"69","author":"H Li","year":"2014","unstructured":"Li, H., Li, X., Ramanathan, M., et al.: Identifying informative risk factors and predicting bone disease progression via deep belief networks. Methods 69(3), 257\u2013265 (2014)","journal-title":"Methods"},{"issue":"4","key":"17_CR8","doi-asserted-by":"publisher","first-page":"492","DOI":"10.15837\/ijccc.2018.4.3269","volume":"13","author":"K Lim","year":"2018","unstructured":"Lim, K., Lee, B.M., Kang, U., et al.: An optimized DBN-based coronary heart disease risk prediction. Int. J. Comput. Commun. Control 13(4), 492\u2013502 (2018)","journal-title":"Int. J. Comput. Commun. Control"},{"issue":"8","key":"17_CR9","first-page":"2806","volume":"29","author":"Z Sun","year":"2012","unstructured":"Sun, Z., Xue, L., Xu, Y., et al.: Overview of deep learning. Appl. Res. Comput. 29(8), 2806\u20132810 (2012)","journal-title":"Appl. Res. Comput."},{"issue":"12","key":"17_CR10","first-page":"115","volume":"29","author":"J Hu","year":"2013","unstructured":"Hu, J., Zhang, G.: K-fold cross-validation based selected ensemble classification algorithm. Bull. Sci. Technol. 29(12), 115\u2013117 (2013)","journal-title":"Bull. Sci. Technol."},{"key":"17_CR11","unstructured":"Yang, X.: Study of early warning for cerebrovascular risk based on deep beliefs networks. Beijing Jiaotong University (2016)"},{"issue":"6","key":"17_CR12","doi-asserted-by":"publisher","first-page":"3041","DOI":"10.1016\/j.eswa.2013.10.034","volume":"41","author":"S Lee","year":"2014","unstructured":"Lee, S., Choeh, J.Y.: Predicting the helpfulness of online reviews using multilayer perceptron neural networks. Expert Syst. Appl. 41(6), 3041\u20133046 (2014)","journal-title":"Expert Syst. Appl."},{"issue":"3","key":"17_CR13","first-page":"616","volume":"35","author":"Y Gao","year":"2014","unstructured":"Gao, Y., Hu, Y.: Influence of different health education modes on self-management level of diabetic patients. Jilin Med. J. 35(3), 616 (2014)","journal-title":"Jilin Med. J."},{"issue":"11","key":"17_CR14","first-page":"1948","volume":"25","author":"S Wu","year":"2014","unstructured":"Wu, S.: Application effect of health education in clinical nursing of diabetic patients. J. Tradit. Chin. Med. Manag. 25(11), 1948\u20131949 (2014)","journal-title":"J. Tradit. Chin. Med. Manag."},{"issue":"24","key":"17_CR15","first-page":"3387","volume":"35","author":"F Zhao","year":"2016","unstructured":"Zhao, F., Luo, J., Wang, Y., et al.: Analysis of the influencing on clinical health education management effect of diabetes. Int. J. Nurs. 35(24), 3387\u20133392 (2016)","journal-title":"Int. J. Nurs."},{"issue":"6","key":"17_CR16","doi-asserted-by":"publisher","first-page":"302","DOI":"10.1097\/00002800-200511000-00009","volume":"19","author":"M Scollan-Koliopoulos","year":"2005","unstructured":"Scollan-Koliopoulos, M., O\u2019Connell, K.A., Walker, E.A.: The first diabetes educator is the family: using illness representation to recognize a multigenerational legacy of diabetes. Clin. Nurse Spec. 19(6), 302\u2013307 (2005)","journal-title":"Clin. Nurse Spec."},{"issue":"9626","key":"17_CR17","doi-asserted-by":"publisher","first-page":"1783","DOI":"10.1016\/S0140-6736(08)60766-7","volume":"371","author":"G Li","year":"2008","unstructured":"Li, G., Zhang, P., Wang, J., et al.: The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet 371(9626), 1783\u20131789 (2008)","journal-title":"Lancet"},{"issue":"10","key":"17_CR18","doi-asserted-by":"publisher","first-page":"2146","DOI":"10.2337\/dc10-0410","volume":"33","author":"T Saaristo","year":"2010","unstructured":"Saaristo, T., Moilanen, L., Korpihy\u00f6v\u00e4lti, E., et al.: Lifestyle intervention for prevention of type 2 diabetes in primary health care. One-year follow-up of the Finnish National Diabetes Prevention Program (FIN-D2D). Diabetes Care 33(10), 2146\u20132151 (2010)","journal-title":"Diabetes Care"},{"issue":"10","key":"17_CR19","first-page":"12","volume":"33","author":"L Xu","year":"2018","unstructured":"Xu, L., Liu, S., Chen, S., et al.: Readiness for hospital discharge and its influencing factors among diabetes patients. J. Nurs. Sci. 33(10), 12\u201315 (2018)","journal-title":"J. Nurs. Sci."},{"issue":"25","key":"17_CR20","first-page":"3558","volume":"22","author":"W Fang","year":"2016","unstructured":"Fang, W., Li, Y.: Correlation analysis between the readiness for hospital discharge and social support status in diabetic patients. Chin. J. Mod. Nurs. 22(25), 3558\u20133561 (2016)","journal-title":"Chin. J. Mod. Nurs."},{"issue":"18","key":"17_CR21","first-page":"251","volume":"11","author":"R Fang","year":"2009","unstructured":"Fang, R., Xia, X.: Effect evaluation of whole course diabetes health education in hospital community integration. Chin. Community Doct.: Med. Spec. 11(18), 251 (2009)","journal-title":"Chin. Community Doct.: Med. Spec."}],"container-title":["Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering","Wireless and Satellite Systems"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-19156-6_17","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,5,21]],"date-time":"2019-05-21T01:32:21Z","timestamp":1558402341000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-19156-6_17"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030191559","9783030191566"],"references-count":21,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-19156-6_17","relation":{},"ISSN":["1867-8211","1867-822X"],"issn-type":[{"type":"print","value":"1867-8211"},{"type":"electronic","value":"1867-822X"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"7 May 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"WiSATS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Wireless and Satellite Systems","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2019","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12 January 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 January 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"10","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"psats2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/psats.eu\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"http:\/\/confy.eai.eu\/","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"289","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"138","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"48% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information"}}]}}