{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T22:53:41Z","timestamp":1743116021769,"version":"3.40.3"},"publisher-location":"Cham","reference-count":16,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031217524"},{"type":"electronic","value":"9783031217531"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-21753-1_3","type":"book-chapter","created":{"date-parts":[[2022,11,20]],"date-time":"2022-11-20T10:02:32Z","timestamp":1668938552000},"page":"22-30","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Characterizing Cardiovascular Risk Through Unsupervised and\u00a0Interpretable Techniques"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4742-5095","authenticated-orcid":false,"given":"Hugo","family":"Calero-D\u00edaz","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5585-2305","authenticated-orcid":false,"given":"David","family":"Chushig-Muzo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5817-989X","authenticated-orcid":false,"given":"Cristina","family":"Soguero-Ruiz","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,11,21]]},"reference":[{"key":"3_CR1","unstructured":"Harris, R.E.: Epidemiology of chronic disease: global perspectives. Jones Bartlett Learn. (2019)"},{"issue":"1","key":"3_CR2","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/1475-2840-12-156","volume":"12","author":"O Schnell","year":"2013","unstructured":"Schnell, O., et al.: Type 1 diabetes and cardiovascular disease. Cardiovasc. Diabetol. 12(1), 1\u201310 (2013)","journal-title":"Cardiovasc. Diabetol."},{"issue":"18","key":"3_CR3","doi-asserted-by":"publisher","first-page":"1837","DOI":"10.1161\/01.CIR.97.18.1837","volume":"97","author":"PW Wilson","year":"1998","unstructured":"Wilson, P.W., et al.: Prediction of coronary heart disease using risk factor categories. Circulation 97(18), 1837\u20131847 (1998)","journal-title":"Circulation"},{"issue":"11","key":"3_CR4","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.1161\/CIRCULATIONAHA.115.018844","volume":"133","author":"D Vistisen","year":"2016","unstructured":"Vistisen, D., et al.: Prediction of first cardiovascular disease event in type 1 diabetes mellitus: the steno type 1 risk engine. Circulation 133(11), 1058\u20131066 (2016)","journal-title":"Circulation"},{"issue":"14","key":"3_CR5","doi-asserted-by":"publisher","first-page":"1156","DOI":"10.1136\/heartjnl-2017-311198","volume":"104","author":"K Shameer","year":"2018","unstructured":"Shameer, K., et al.: Machine learning in cardiovascular medicine: are we there yet? Heart 104(14), 1156\u20131164 (2018)","journal-title":"Heart"},{"issue":"1","key":"3_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s13040-022-00303-z","volume":"15","author":"D Chushig-Muzo","year":"2022","unstructured":"Chushig-Muzo, D., et al.: Learning and visualizing chronic latent representations using electronic health records. BioData Mining 15(1), 1\u201327 (2022)","journal-title":"BioData Mining"},{"issue":"4","key":"3_CR7","doi-asserted-by":"publisher","first-page":"S1","DOI":"10.1016\/j.kint.2020.06.019","volume":"98","author":"IH de Boer","year":"2020","unstructured":"de Boer, I.H., et al.: Kdigo 2020 clinical practice guideline for diabetes management in chronic kidney disease. Kidney Int. 98(4), S1\u2013S115 (2020)","journal-title":"Kidney Int."},{"issue":"1","key":"3_CR8","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0210236","volume":"14","author":"MZ Rodriguez","year":"2019","unstructured":"Rodriguez, M.Z., et al.: Clustering algorithms: a comparative approach. PLoS ONE 14(1), e0210236 (2019)","journal-title":"PLoS ONE"},{"issue":"18","key":"3_CR9","doi-asserted-by":"publisher","first-page":"3832","DOI":"10.1016\/j.neucom.2011.07.014","volume":"74","author":"C-C Hsu","year":"2011","unstructured":"Hsu, C.-C., Lin, S.-H., Tai, W.-S.: Apply extended self-organizing map to cluster and classify mixed-type data. Neurocomputing 74(18), 3832\u20133842 (2011)","journal-title":"Neurocomputing"},{"issue":"1","key":"3_CR10","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1111\/insr.12274","volume":"87","author":"AH Foss","year":"2019","unstructured":"Foss, A.H., Markatou, M., Ray, B.: Distance metrics and clustering methods for mixed-type data. Int. Stat. Rev. 87(1), 80\u2013109 (2019)","journal-title":"Int. Stat. Rev."},{"issue":"1","key":"3_CR11","doi-asserted-by":"publisher","first-page":"243","DOI":"10.1016\/j.patcog.2012.07.021","volume":"46","author":"O Arbelaitz","year":"2013","unstructured":"Arbelaitz, O., et al.: An extensive comparative study of cluster validity indices. Pattern Recogn. 46(1), 243\u2013256 (2013)","journal-title":"Pattern Recogn."},{"key":"3_CR12","unstructured":"Frost, N., Moshkovitz, M., Rashtchian, C.: Exkmc: expanding explainable $$k$$-means clustering, arXiv preprint arXiv:2006.02399 (2020)"},{"issue":"6","key":"3_CR13","doi-asserted-by":"publisher","first-page":"442","DOI":"10.1016\/j.diabet.2020.09.001","volume":"46","author":"B Verg\u00e8s","year":"2020","unstructured":"Verg\u00e8s, B.: Cardiovascular disease in type 1 diabetes: a review of epidemiological data and underlying mechanisms. Diabetes Metabolism 46(6), 442\u2013449 (2020)","journal-title":"Diabetes Metabolism"},{"issue":"4","key":"3_CR14","doi-asserted-by":"publisher","first-page":"421","DOI":"10.1001\/jama.286.4.421","volume":"286","author":"H Gerstein","year":"2001","unstructured":"Gerstein, H., et al.: Albuminuria and risk of cardiovascular events, death, and heart failure in diabetic and nondiabetic individuals. JAMA 286(4), 421\u2013426 (2001)","journal-title":"JAMA"},{"issue":"10","key":"3_CR15","doi-asserted-by":"publisher","first-page":"1213","DOI":"10.1111\/j.1464-5491.2011.03342.x","volume":"28","author":"J Cederholm","year":"2011","unstructured":"Cederholm, J., et al.: A new model for 5-year risk of cardiovascular disease in type 1 diabetes; from the swedish national diabetes register (ndr). Diabet. Med. 28(10), 1213\u20131220 (2011)","journal-title":"Diabet. Med."},{"key":"3_CR16","doi-asserted-by":"crossref","unstructured":"Montavon, G., Samek, W., M\u00fcller, K.R.: Methods for interpreting and understanding deep neural networks. Digit. Sig. Proc. 73, 1\u201315 (2018)","DOI":"10.1016\/j.dsp.2017.10.011"}],"container-title":["Lecture Notes in Computer Science","Intelligent Data Engineering and Automated Learning \u2013 IDEAL 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-21753-1_3","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T12:11:53Z","timestamp":1710331913000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-21753-1_3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031217524","9783031217531"],"references-count":16,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-21753-1_3","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"21 November 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IDEAL","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Intelligent Data Engineering and Automated Learning","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Manchester","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 November 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"26 November 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ideal2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/ideal-conf.com\/","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 (provided by the conference organizers)"}},{"value":"Easychair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"79","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"52","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"66% - 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 (provided by the conference organizers)"}},{"value":"2.9","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"2.1","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}