{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,24]],"date-time":"2025-06-24T07:08:59Z","timestamp":1750748939380,"version":"3.40.3"},"publisher-location":"Cham","reference-count":36,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030304928"},{"type":"electronic","value":"9783030304935"}],"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\/978-3-030-30493-5_34","type":"book-chapter","created":{"date-parts":[[2019,9,10]],"date-time":"2019-09-10T20:03:41Z","timestamp":1568145821000},"page":"338-350","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction"],"prefix":"10.1007","author":[{"given":"Zakhriya","family":"Alhassan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"David","family":"Budgen","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ali","family":"Alessa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Riyad","family":"Alshammari","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tahani","family":"Daghstani","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Noura","family":"Al Moubayed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2019,9,9]]},"reference":[{"issue":"1","key":"34_CR1","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1016\/j.amepre.2010.09.022","volume":"40","author":"RT Ackermann","year":"2011","unstructured":"Ackermann, R.T., Cheng, Y.J., Williamson, D.F., Gregg, E.W.: Identifying adults at high risk for diabetes and cardiovascular disease using hemoglobin A1c: national health and nutrition examination survey 2005\u20132006. Am. J. Prev. Med. 40(1), 11\u201317 (2011)","journal-title":"Am. J. Prev. Med."},{"key":"34_CR2","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"423","DOI":"10.1007\/978-3-319-44781-0_50","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2016","author":"N Al Moubayed","year":"2016","unstructured":"Al Moubayed, N., Breckon, T., Matthews, P., McGough, A.S.: SMS spam filtering using probabilistic topic modelling and stacked denoising autoencoder. In: Villa, A.E.P., Masulli, P., Pons Rivero, A.J. (eds.) ICANN 2016. LNCS, vol. 9887, pp. 423\u2013430. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-44781-0_50"},{"key":"34_CR3","doi-asserted-by":"crossref","unstructured":"Alhassan, Z., Budgen, D., Alshammari, R., Daghstani, T., McGough, A.S., Al Moubayed, N.: Stacked denoising autoencoders for mortality risk prediction using imbalanced clinical data. In: International Conference on Machine Learning and Applications. IEEE (2018)","DOI":"10.1109\/ICMLA.2018.00087"},{"key":"34_CR4","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"468","DOI":"10.1007\/978-3-030-01424-7_46","volume-title":"Artificial Neural Networks and Machine Learning \u2013 ICANN 2018","author":"Z Alhassan","year":"2018","unstructured":"Alhassan, Z., McGough, A.S., Alshammari, R., Daghstani, T., Budgen, D., Al Moubayed, N.: Type-2 diabetes mellitus diagnosis from time series clinical data using deep learning models. In: K\u016frkov\u00e1, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds.) ICANN 2018. LNCS, vol. 11141, pp. 468\u2013478. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01424-7_46"},{"key":"34_CR5","first-page":"1","volume":"2","author":"J An","year":"2015","unstructured":"An, J., Cho, S.: Variational autoencoder based anomaly detection using reconstruction probability. Spec. Lect. IE 2, 1\u201318 (2015)","journal-title":"Spec. Lect. IE"},{"issue":"Supplement 1","key":"34_CR6","doi-asserted-by":"publisher","first-page":"S81","DOI":"10.2337\/dc14-S081","volume":"37","author":"AD Association","year":"2014","unstructured":"Association, A.D., et al.: Diagnosis and classification of diabetes mellitus. Diabetes Care 37(Supplement 1), S81\u2013S90 (2014)","journal-title":"Diabetes Care"},{"key":"34_CR7","doi-asserted-by":"crossref","unstructured":"Baur, C., Wiestler, B., Albarqouni, S., Navab, N.: Deep autoencoding models for unsupervised anomaly segmentation in brain MR images. arXiv preprint arXiv:1804.04488 (2018)","DOI":"10.1007\/978-3-030-11723-8_16"},{"key":"34_CR8","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/978-3-642-00296-0","volume-title":"Noise Reduction in Speech Processing","author":"J Benesty","year":"2009","unstructured":"Benesty, J., Chen, J., Huang, Y., Cohen, I.: Pearson correlation coefficient. In: Cohen, I., Huang, Y., Chen, J., Benesty, J. (eds.) Noise Reduction in Speech Processing, pp. 1\u20134. Springer, Heidelberg (2009). https:\/\/doi.org\/10.1007\/978-3-642-00296-0"},{"issue":"2","key":"34_CR9","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1109\/72.279181","volume":"5","author":"Y Bengio","year":"1994","unstructured":"Bengio, Y., Simard, P., Frasconi, P.: Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5(2), 157\u2013166 (1994)","journal-title":"IEEE Trans. Neural Netw."},{"issue":"Supplement 2","key":"34_CR10","doi-asserted-by":"publisher","first-page":"S184","DOI":"10.2337\/dc11-s216","volume":"34","author":"E Bonora","year":"2011","unstructured":"Bonora, E., Tuomilehto, J.: The pros and cons of diagnosing diabetes with A1c. Diabetes Care 34(Supplement 2), S184\u2013S190 (2011)","journal-title":"Diabetes Care"},{"issue":"7","key":"34_CR11","doi-asserted-by":"publisher","first-page":"1327","DOI":"10.2337\/dc09-9033","volume":"32","author":"IE Committee","year":"2009","unstructured":"Committee, I.E., et al.: International expert committee report on the role of the A1c assay in the diagnosis of diabetes. Diabetes Care 32(7), 1327\u20131334 (2009)","journal-title":"Diabetes Care"},{"issue":"12","key":"34_CR12","doi-asserted-by":"publisher","first-page":"1175","DOI":"10.1111\/j.1525-1497.2004.40178.x","volume":"19","author":"D Edelman","year":"2004","unstructured":"Edelman, D., Olsen, M.K., Dudley, T.K., Harris, A.C., Oddone, E.Z.: Utility of hemoglobin A1c in predicting diabetes risk. J. Gen. Intern. Med. 19(12), 1175\u20131180 (2004)","journal-title":"J. Gen. Intern. Med."},{"key":"34_CR13","unstructured":"Federation ID: IDF diabetes atlas (2017). http:\/\/www.diabetesatlas.org"},{"issue":"10","key":"34_CR14","doi-asserted-by":"publisher","first-page":"2108","DOI":"10.1109\/TIFS.2015.2446438","volume":"10","author":"S Gao","year":"2015","unstructured":"Gao, S., Zhang, Y., Jia, K., Lu, J., Zhang, Y.: Single sample face recognition via learning deep supervised autoencoders. IEEE Trans. Inf. Forensics Secur. 10(10), 2108\u20132118 (2015)","journal-title":"IEEE Trans. Inf. Forensics Secur."},{"issue":"3","key":"34_CR15","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1016\/j.diabres.2007.05.004","volume":"78","author":"HC Gerstein","year":"2007","unstructured":"Gerstein, H.C., et al.: Annual incidence and relative risk of diabetes in people with various categories of dysglycemia: a systematic overview and meta-analysis of prospective studies. Diabetes Res. Clin. Pract. 78(3), 305\u2013312 (2007)","journal-title":"Diabetes Res. Clin. Pract."},{"key":"34_CR16","doi-asserted-by":"crossref","unstructured":"Gondara, L.: Medical image denoising using convolutional denoising autoencoders. In: 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), pp. 241\u2013246, December 2016","DOI":"10.1109\/ICDMW.2016.0041"},{"key":"34_CR17","volume-title":"Deep Learning","author":"I Goodfellow","year":"2016","unstructured":"Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)"},{"key":"34_CR18","doi-asserted-by":"crossref","unstructured":"UK Prospective Diabetes Study Group, et al.: Tight blood pressure control and risk of macrovascular and microvascular complications in type 2 diabetes: UKPDS 38. BMJ: Br. Med. J. 317(7160), 703 (1998)","DOI":"10.1136\/bmj.317.7160.703"},{"key":"34_CR19","unstructured":"Hinton, G.E., Zemel, R.S.: Autoencoders, minimum description length, and Helmholtz free energy. In: Advances in Neural Information Processing Systems, p. 3 (1994)"},{"issue":"8","key":"34_CR20","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"key":"34_CR21","doi-asserted-by":"publisher","first-page":"13058","DOI":"10.1038\/srep13058","volume":"5","author":"J K\u00e4lsch","year":"2015","unstructured":"K\u00e4lsch, J., et al.: Normal liver enzymes are correlated with severity of metabolic syndrome in a large population based cohort. Sci. Rep. 5, 13058 (2015)","journal-title":"Sci. Rep."},{"issue":"10","key":"34_CR22","first-page":"1274","volume":"5","author":"E Kazemi","year":"2014","unstructured":"Kazemi, E., Hosseini, S.M., Bahrampour, A., Faghihimani, E., Amini, M.: Predicting of trend of hemoglobin A1c in type 2 diabetes: a longitudinal linear mixed model. Int. J. Prev. Med. 5(10), 1274 (2014)","journal-title":"Int. J. Prev. Med."},{"issue":"6","key":"34_CR23","doi-asserted-by":"publisher","first-page":"413","DOI":"10.7326\/0003-4819-141-6-200409210-00006","volume":"141","author":"KT Khaw","year":"2004","unstructured":"Khaw, K.T., Wareham, N., Bingham, S., Luben, R., Welch, A., Day, N.: Association of hemoglobin A1c with cardiovascular disease and mortality in adults: the European prospective investigation into cancer in Norfolk. Ann. Intern. Med. 141(6), 413\u2013420 (2004)","journal-title":"Ann. Intern. Med."},{"issue":"8","key":"34_CR24","doi-asserted-by":"publisher","first-page":"417","DOI":"10.1056\/NEJM197608192950804","volume":"295","author":"RJ Koenig","year":"1976","unstructured":"Koenig, R.J., Peterson, C.M., Jones, R.L., Saudek, C., Lehrman, M., Cerami, A.: Correlation of glucose regulation and hemoglobin AIc in diabetes mellitus. N. Engl. J. Med. 295(8), 417\u2013420 (1976)","journal-title":"N. Engl. J. Med."},{"issue":"15","key":"34_CR25","doi-asserted-by":"publisher","first-page":"1021","DOI":"10.1056\/NEJM199010113231503","volume":"323","author":"ML Larsen","year":"1990","unstructured":"Larsen, M.L., H\u00f8rder, M., Mogensen, E.F.: Effect of long-term monitoring of glycosylated hemoglobin levels in insulin-dependent diabetes mellitus. N. Engl. J. Med. 323(15), 1021\u20131025 (1990)","journal-title":"N. Engl. J. Med."},{"issue":"7553","key":"34_CR26","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"issue":"Nov","key":"34_CR27","first-page":"2579","volume":"9","author":"LVD Maaten","year":"2008","unstructured":"Maaten, L.V.D., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(Nov), 2579\u20132605 (2008)","journal-title":"J. Mach. Learn. Res."},{"issue":"2","key":"34_CR28","doi-asserted-by":"publisher","first-page":"352","DOI":"10.2337\/diacare.29.02.06.dc05-1594","volume":"29","author":"RJ McCarter","year":"2006","unstructured":"McCarter, R.J., Hempe, J.M., Chalew, S.A.: Mean blood glucose and biological variation have greater influence on HbA1c levels than glucose instability: an analysis of data from the diabetes control and complications trial. Diabetes Care 29(2), 352\u2013355 (2006)","journal-title":"Diabetes Care"},{"key":"34_CR29","doi-asserted-by":"publisher","first-page":"1473","DOI":"10.2337\/dc08-0545","volume":"31","author":"DM Nathan","year":"2008","unstructured":"Nathan, D.M., et al.: Translating the A1c assay into estimated average glucose values. Diabetes Care 31, 1473\u20131478 (2008)","journal-title":"Diabetes Care"},{"issue":"9","key":"34_CR30","doi-asserted-by":"crossref","first-page":"1951","DOI":"10.1093\/clinchem\/44.9.1951","volume":"44","author":"KP Peterson","year":"1998","unstructured":"Peterson, K.P., Pavlovich, J.G., Goldstein, D., Little, R., England, J., Peterson, C.M.: What is hemoglobin A1c? An analysis of glycated hemoglobins by electrospray ionization mass spectrometry. Clin. Chem. 44(9), 1951\u20131958 (1998)","journal-title":"Clin. Chem."},{"issue":"8","key":"34_CR31","doi-asserted-by":"publisher","first-page":"720","DOI":"10.1016\/j.amjmed.2007.03.022","volume":"120","author":"AD Pradhan","year":"2007","unstructured":"Pradhan, A.D., Rifai, N., Buring, J.E., Ridker, P.M.: Hemoglobin A1c predicts diabetes but not cardiovascular disease in nondiabetic women. Am. J. Med. 120(8), 720\u2013727 (2007)","journal-title":"Am. J. Med."},{"key":"34_CR32","unstructured":"Rose, E., Ketchell, D.S.: Does daily monitoring of blood glucose predict hemoglobin A1c levels? Clinical Inquiries, 2003 (MU) (2003)"},{"key":"34_CR33","doi-asserted-by":"crossref","unstructured":"Shin, H., Orton, M., Collins, D.J., Doran, S., Leach, M.O.: Autoencoder in time-series analysis for unsupervised tissues characterisation in a large unlabelled medical image dataset. In: 2011 10th International Conference on Machine Learning and Applications and Workshops, vol. 1, pp. 259\u2013264, December 2011","DOI":"10.1109\/ICMLA.2011.38"},{"issue":"7258","key":"34_CR34","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1136\/bmj.321.7258.405","volume":"321","author":"IM Stratton","year":"2000","unstructured":"Stratton, I.M., et al.: Association of glycaemia with macrovascular and microvascular complications of type 2 diabetes (UKPDS 35): prospective observational study. BMJ 321(7258), 405\u2013412 (2000)","journal-title":"BMJ"},{"issue":"Dec","key":"34_CR35","first-page":"3371","volume":"11","author":"P Vincent","year":"2010","unstructured":"Vincent, P., Larochelle, H., Lajoie, I., Bengio, Y., Manzagol, P.A.: Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res. 11(Dec), 3371\u20133408 (2010)","journal-title":"J. Mach. Learn. Res."},{"issue":"4","key":"34_CR36","doi-asserted-by":"publisher","first-page":"e10780","DOI":"10.2196\/10780","volume":"6","author":"BJ Wells","year":"2018","unstructured":"Wells, B.J., et al.: Predicting current glycated hemoglobin values in adults: development of an algorithm from the electronic health record. JMIR Med. Inform. 6(4), e10780 (2018)","journal-title":"JMIR Med. Inform."}],"container-title":["Lecture Notes in Computer Science","Artificial Neural Networks and Machine Learning \u2013 ICANN 2019: Workshop and Special Sessions"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-30493-5_34","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T16:50:55Z","timestamp":1710348655000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-30493-5_34"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"ISBN":["9783030304928","9783030304935"],"references-count":36,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-30493-5_34","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2019]]},"assertion":[{"value":"9 September 2019","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Munich","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"17 September 2019","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 September 2019","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icann2019","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/e-nns.org\/icann2019\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}