{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T07:31:19Z","timestamp":1743060679548,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030772109"},{"type":"electronic","value":"9783030772116"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"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":[[2021]]},"DOI":"10.1007\/978-3-030-77211-6_37","type":"book-chapter","created":{"date-parts":[[2021,6,7]],"date-time":"2021-06-07T23:06:27Z","timestamp":1623107187000},"page":"329-337","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Recurrent Neural Network to Predict Renal Function Impairment in Diabetic Patients via Longitudinal Routine Check-up Data"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5940-645X","authenticated-orcid":false,"given":"Enrico","family":"Longato","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6510-2097","authenticated-orcid":false,"given":"Gian Paolo","family":"Fadini","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3248-1393","authenticated-orcid":false,"given":"Giovanni","family":"Sparacino","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1177-0516","authenticated-orcid":false,"given":"Angelo","family":"Avogaro","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8415-4688","authenticated-orcid":false,"given":"Barbara","family":"Di Camillo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,6,8]]},"reference":[{"key":"37_CR1","doi-asserted-by":"publisher","first-page":"121","DOI":"10.1053\/j.ackd.2017.10.011","volume":"25","author":"DN Koye","year":"2018","unstructured":"Koye, D.N., Magliano, D.J., Nelson, R.G., Pavkov, M.E.: The global epidemiology of diabetes and kidney disease. Adv. Chron. Kidney Dis. 25, 121\u2013132 (2018). https:\/\/doi.org\/10.1053\/j.ackd.2017.10.011","journal-title":"Adv. Chron. Kidney Dis."},{"key":"37_CR2","doi-asserted-by":"publisher","first-page":"e307","DOI":"10.1016\/S2214-109X(16)00071-1","volume":"4","author":"B Ene-Iordache","year":"2016","unstructured":"Ene-Iordache, B., et al.: Chronic kidney disease and cardiovascular risk in six regions of the world (ISN-KDDC): a cross-sectional study. Lancet Global Health 4, e307\u2013e319 (2016). https:\/\/doi.org\/10.1016\/S2214-109X(16)00071-1","journal-title":"Lancet Global Health"},{"key":"37_CR3","doi-asserted-by":"publisher","first-page":"662","DOI":"10.1016\/j.jfma.2018.02.007","volume":"117","author":"Y-C Lin","year":"2018","unstructured":"Lin, Y.-C., Chang, Y.-H., Yang, S.-Y., Wu, K.-D., Chu, T.-S.: Update of pathophysiology and management of diabetic kidney disease. J. Formosan Med. Assoc. 117, 662\u2013675 (2018). https:\/\/doi.org\/10.1016\/j.jfma.2018.02.007","journal-title":"J. Formosan Med. Assoc."},{"key":"37_CR4","doi-asserted-by":"publisher","first-page":"1660","DOI":"10.2337\/dc13-2036","volume":"37","author":"G Andr\u00e9sd\u00f3ttir","year":"2014","unstructured":"Andr\u00e9sd\u00f3ttir, G., et al.: Improved survival and renal prognosis of patients with type 2 diabetes and nephropathy with improved control of risk factors. Diab. Care 37, 1660\u20131667 (2014). https:\/\/doi.org\/10.2337\/dc13-2036","journal-title":"Diab. Care"},{"key":"37_CR5","doi-asserted-by":"publisher","first-page":"1","DOI":"10.2337\/dbi20-0040","volume":"70","author":"KR Tuttle","year":"2021","unstructured":"Tuttle, K.R., et al.: SGLT2 inhibition for CKD and cardiovascular disease in type 2 diabetes: report of a scientific workshop sponsored by the national kidney foundation. Diabetes 70, 1\u201316 (2021). https:\/\/doi.org\/10.2337\/dbi20-0040","journal-title":"Diabetes"},{"key":"37_CR6","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMoa1811744","author":"V Perkovic","year":"2019","unstructured":"Perkovic, V., et al.: Canagliflozin and renal outcomes in type 2 diabetes and nephropathy. N. Engl. J. Med. (2019). https:\/\/doi.org\/10.1056\/NEJMoa1811744","journal-title":"N. Engl. J. Med."},{"key":"37_CR7","doi-asserted-by":"publisher","first-page":"323","DOI":"10.1056\/NEJMoa1515920","volume":"375","author":"C Wanner","year":"2016","unstructured":"Wanner, C., et al.: Empagliflozin and progression of kidney disease in type 2 diabetes. N. Engl. J. Med. 375, 323\u2013334 (2016). https:\/\/doi.org\/10.1056\/NEJMoa1515920","journal-title":"N. Engl. J. Med."},{"key":"37_CR8","doi-asserted-by":"publisher","first-page":"644","DOI":"10.1056\/NEJMoa1611925","volume":"377","author":"B Neal","year":"2017","unstructured":"Neal, B., et al.: Canagliflozin and cardiovascular and renal events in type 2 diabetes. N. Engl. J. Med. 377, 644\u2013657 (2017). https:\/\/doi.org\/10.1056\/NEJMoa1611925","journal-title":"N. Engl. J. Med."},{"key":"37_CR9","doi-asserted-by":"publisher","first-page":"835","DOI":"10.1007\/s13300-020-00798-x","volume":"11","author":"WL Yin","year":"2020","unstructured":"Yin, W.L., Bain, S.C., Min, T.: The effect of glucagon-like peptide-1 receptor agonists on renal outcomes in type 2 diabetes. Diab. Ther. 11, 835\u2013844 (2020). https:\/\/doi.org\/10.1007\/s13300-020-00798-x","journal-title":"Diab. Ther."},{"key":"37_CR10","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1038\/s41591-018-0239-8","volume":"25","author":"S Ravizza","year":"2019","unstructured":"Ravizza, S., et al.: Predicting the early risk of chronic kidney disease in patients with diabetes using real-world data. Nat. Med. 25, 57\u201359 (2019). https:\/\/doi.org\/10.1038\/s41591-018-0239-8","journal-title":"Nat. Med."},{"key":"37_CR11","doi-asserted-by":"publisher","first-page":"1097","DOI":"10.1111\/nep.13636","volume":"24","author":"C Yang","year":"2019","unstructured":"Yang, C., Kong, G., Wang, L., Zhang, L., Zhao, M.-H.: Big data in nephrology: Are we ready for the change? Nephrology 24, 1097\u20131102 (2019). https:\/\/doi.org\/10.1111\/nep.13636","journal-title":"Nephrology"},{"key":"37_CR12","first-page":"S1","volume":"39","author":"National Kidney Foundation","year":"2002","unstructured":"National Kidney Foundation: K\/DOQI clinical practice guidelines for chronic kidney disease: evaluation, classification, and stratification. Am. J. Kidney Dis. 39, S1\u2013266 (2002)","journal-title":"Am. J. Kidney Dis."},{"key":"37_CR13","unstructured":"Gal, Y., Ghahramani, Z.: A theoretically grounded application of dropout in recurrent neural networks. arXiv:1512.05287 [stat]. (2016)"},{"key":"37_CR14","doi-asserted-by":"publisher","first-page":"904","DOI":"10.1177\/0272989X18801312","volume":"38","author":"A Bansal","year":"2018","unstructured":"Bansal, A., Heagerty, P.J.: A tutorial on evaluating the time-varying discrimination accuracy of survival models used in dynamic decision making. Med. Decis. Making 38, 904\u2013916 (2018). https:\/\/doi.org\/10.1177\/0272989X18801312","journal-title":"Med. Decis. Making"},{"key":"37_CR15","doi-asserted-by":"publisher","first-page":"391","DOI":"10.2337\/dc16-2202","volume":"40","author":"G Mayer","year":"2017","unstructured":"Mayer, G., et al.: Systems biology-derived biomarkers to predict progression of renal function decline in type 2 diabetes. Diab. Care 40, 391\u2013397 (2017). https:\/\/doi.org\/10.2337\/dc16-2202","journal-title":"Diab. Care"},{"key":"37_CR16","doi-asserted-by":"publisher","first-page":"837","DOI":"10.2307\/2531595","volume":"44","author":"ER DeLong","year":"1988","unstructured":"DeLong, E.R., DeLong, D.M., Clarke-Pearson, D.L.: Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44, 837\u2013845 (1988). https:\/\/doi.org\/10.2307\/2531595","journal-title":"Biometrics"},{"key":"37_CR17","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1177\/1932296817706375","volume":"12","author":"A Dagliati","year":"2018","unstructured":"Dagliati, A., et al.: Machine learning methods to predict diabetes complications. J. Diab. Sci Technol. 12, 295\u2013302 (2018). https:\/\/doi.org\/10.1177\/1932296817706375","journal-title":"J. Diab. Sci Technol."},{"key":"37_CR18","doi-asserted-by":"publisher","unstructured":"Retnakaran, R., Cull, C.A., Thorne, K.I., Adler, A.I., Holman, R.R.: Risk factors for renal dysfunction in type 2 diabetes: U.K. Prospective diabetes study 74. Diabetes 55, 1832\u20131839 (2006). https:\/\/doi.org\/10.2337\/db05-1620","DOI":"10.2337\/db05-1620"},{"key":"37_CR19","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"1999","unstructured":"Gers, F.A., Schmidhuber, J., Cummins, F.: Learning to forget: continual prediction with LSTM. Neural Comput. 12, 2451\u20132471 (1999)","journal-title":"Neural Comput."}],"container-title":["Lecture Notes in Computer Science","Artificial Intelligence in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-77211-6_37","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T10:07:15Z","timestamp":1706695635000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-77211-6_37"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030772109","9783030772116"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-77211-6_37","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"8 June 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"AIME","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Intelligence in Medicine","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 June 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"aime2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/aime21.aimedicine.info\/index.php","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}