{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T11:36:48Z","timestamp":1781264208908,"version":"3.54.1"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T00:00:00Z","timestamp":1572998400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["BMC Med Inform Decis Mak"],"published-print":{"date-parts":[[2019,12]]},"abstract":"<jats:title>Abstract<\/jats:title>\n              <jats:sec>\n                <jats:title>Background<\/jats:title>\n                <jats:p>For an effective artificial pancreas (AP) system and an improved therapeutic intervention with continuous glucose monitoring (CGM), predicting the occurrence of hypoglycemia accurately is very important. While there have been many studies reporting successful algorithms for predicting nocturnal hypoglycemia, predicting postprandial hypoglycemia still remains a challenge due to extreme glucose fluctuations that occur around mealtimes. The goal of this study is to evaluate the feasibility of easy-to-use, computationally efficient machine-learning algorithm to predict postprandial hypoglycemia with a unique feature set.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Methods<\/jats:title>\n                <jats:p>We use retrospective CGM datasets of 104 people who had experienced at least one hypoglycemia alert value during a three-day CGM session. The algorithms were developed based on four machine learning models with a unique data-driven feature set: a random forest (RF), a support vector machine using a linear function or a radial basis function, a K-nearest neighbor, and a logistic regression. With 5-fold cross-subject validation, the average performance of each model was calculated to compare and contrast their individual performance. The area under a receiver operating characteristic curve (AUC) and the F1 score were used as the main criterion for evaluating the performance.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>In predicting a hypoglycemia alert value with a 30-min prediction horizon, the RF model showed the best performance with the average AUC of 0.966, the average sensitivity of 89.6%, the average specificity of 91.3%, and the average F1 score of 0.543. In addition, the RF showed the better predictive performance for postprandial hypoglycemic events than other models.<\/jats:p>\n              <\/jats:sec>\n              <jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>In conclusion, we showed that machine-learning algorithms have potential in predicting postprandial hypoglycemia, and the RF model could be a better candidate for the further development of postprandial hypoglycemia prediction algorithm to advance the CGM technology and the AP technology further.<\/jats:p>\n              <\/jats:sec>","DOI":"10.1186\/s12911-019-0943-4","type":"journal-article","created":{"date-parts":[[2019,11,6]],"date-time":"2019-11-06T12:03:12Z","timestamp":1573041792000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["A machine-learning approach to predict postprandial hypoglycemia"],"prefix":"10.1186","volume":"19","author":[{"given":"Wonju","family":"Seo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"You-Bin","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seunghyun","family":"Lee","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sang-Man","family":"Jin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8359-8110","authenticated-orcid":false,"given":"Sung-Min","family":"Park","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2019,11,6]]},"reference":[{"key":"943_CR1","doi-asserted-by":"publisher","first-page":"9","DOI":"10.2337\/dc13-2112","volume":"37","author":"DM Nathan","year":"2014","unstructured":"Nathan DM, DCCT\/Edic Research Group. The diabetes control and complications trial\/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care. 2014; 37:9\u201316.","journal-title":"Diabetes Care"},{"key":"943_CR2","doi-asserted-by":"publisher","first-page":"824","DOI":"10.1056\/NEJMoa1206881","volume":"368","author":"M Phillip","year":"2013","unstructured":"Phillip M, Battelino T, Atlas E, Kordonouri O, Bratina N, Miller S, et al.Nocturnal glucose control with an artificial pancreas at a diabetes camp. N Engl J Med. 2013; 368:824\u201333.","journal-title":"N Engl J Med"},{"key":"943_CR3","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1056\/NEJMoa1509351","volume":"373","author":"H Thabit","year":"2015","unstructured":"Thabit H, Tauschmann M, Allen J, Leelarathna L, Hartnell S, Wilinska M, et al.Home Use of an Artificial Beta Cell in Type 1 Diabetes. New Engl J Med. 2015; 373:2129\u201340.","journal-title":"New Engl J Med"},{"key":"943_CR4","doi-asserted-by":"publisher","first-page":"2643","DOI":"10.1056\/NEJMoa052187","volume":"353","author":"Diabetes Control and Complications Trial\/Epidemiology of Diabetes Interventions and Complications (DCCT\/EDIC) Study Research Group","year":"2005","unstructured":"Diabetes Control and Complications Trial\/Epidemiology of Diabetes Interventions and Complications (DCCT\/EDIC) Study Research Group. Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes. N Engl J Med. 2005; 353:2643\u201353.","journal-title":"N Engl J Med"},{"key":"943_CR5","doi-asserted-by":"publisher","first-page":"3169","DOI":"10.2337\/db08-1084","volume":"57","author":"PE Cryer","year":"2008","unstructured":"Cryer PE. The barrier of hypoglycemia in diabetes. Diabetes. 2008; 57:3169\u201376.","journal-title":"Diabetes"},{"key":"943_CR6","doi-asserted-by":"publisher","first-page":"313","DOI":"10.1056\/NEJMoa1314474","volume":"371","author":"SJ Russell","year":"2014","unstructured":"Russell SJ, El-Khatib FH, Sinha M, Magyar KL, McKeon K, Goergen LG, et al.Outpatient Glycemic Control with a Bionic Pancreas in Type 1 Diabetes. N Engl J Med. 2014; 371:313\u201325.","journal-title":"N Engl J Med"},{"key":"943_CR7","doi-asserted-by":"publisher","first-page":"1184","DOI":"10.2337\/dc13-2066","volume":"37","author":"YC Kudva","year":"2014","unstructured":"Kudva YC, Carter RE, Cobelli C, Basu R, Basu A. Closed-loop artificial pancreas systems: Physiological input to enhance next-generation devices. Diabetes Care. 2014; 37:1184\u201390.","journal-title":"Diabetes Care"},{"issue":"7","key":"943_CR8","doi-asserted-by":"publisher","first-page":"1123","DOI":"10.2337\/dc16-0824","volume":"39","author":"B Kovatchev","year":"2016","unstructured":"Kovatchev B, Tamborlane WV, Cefalu WT, Cobelli C. The artificial pancreas in 2016: A digital treatment ecosystem for diabetes. Diabetes Care. 2016; 39(7):1123\u201326. \n                    https:\/\/doi.org\/10.2337\/dc16-0824\n                    \n                  .","journal-title":"Diabetes Care"},{"key":"943_CR9","doi-asserted-by":"publisher","first-page":"1407","DOI":"10.1001\/jama.2016.11708","volume":"316","author":"RM Bergenstal","year":"2016","unstructured":"Bergenstal RM, Garg S, Weinzimer SA, Buckingham BA, Bode BW, Tamborlane WV, et al.Safety of a Hybrid Closed-Loop Insulin Delivery System in Patients With Type 1 Diabetes. JAMA. 2016; 316:1407.","journal-title":"JAMA"},{"issue":"1","key":"943_CR10","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1177\/1932296814546668","volume":"9","author":"D Rylander","year":"2015","unstructured":"Rylander D. Glucagon in the Artificial Pancreas: Supply and Marketing Challenges. J Diabetes Sci Technol. 2015; 9(1):52\u201355. \n                    https:\/\/doi.org\/10.1177\/1932296814546668\n                    \n                  .","journal-title":"J Diabetes Sci Technol"},{"issue":"1","key":"943_CR11","doi-asserted-by":"publisher","first-page":"8","DOI":"10.1177\/1932296814555541","volume":"8","author":"R Pohl","year":"2015","unstructured":"Pohl R, Li M, Krasner A, De Souza E. Development of stable liquid glucagon formulations for use in artificial pancreas. J Diabetes Sci Technol. 2015; 8(1):8\u201316.","journal-title":"J Diabetes Sci Technol"},{"issue":"1","key":"943_CR12","doi-asserted-by":"publisher","first-page":"214","DOI":"10.1210\/jc.2015-3003","volume":"101","author":"A Haidar","year":"2016","unstructured":"Haidar A, Rabasa-Lhoret R, Legault L, Lovblom LE, Rakheja R, Messier V, D\u2019Aoust \u00c9, Falappa CM, Justice T, Orszag A. Single- and dual-hormone artificial pancreas for overnight glucose control in type 1 diabetes. J Clin Endocrinol Metab. 2016; 101(1):214\u201323.","journal-title":"J Clin Endocrinol Metab"},{"issue":"11","key":"943_CR13","doi-asserted-by":"publisher","first-page":"1011","DOI":"10.1111\/dom.12501","volume":"17","author":"PD Home","year":"2015","unstructured":"Home PD. Plasma insulin profiles after subcutaneous injection: How close can we get to physiology in people with diabetes?Diabetes Obes Metab. 2015; 17(11):1011\u201320. \n                    https:\/\/doi.org\/10.1111\/dom.12501\n                    \n                  .","journal-title":"Diabetes Obes Metab"},{"key":"943_CR14","doi-asserted-by":"publisher","first-page":"16009","DOI":"10.1073\/pnas.1006639107","volume":"107","author":"RH Unger","year":"2010","unstructured":"Unger RH, Orci L. Paracrinology of islets and the paracrinopathy of diabetes. Proc Natl Acad Sci. 2010; 107:16009\u201312.","journal-title":"Proc Natl Acad Sci"},{"issue":"6","key":"943_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1002\/cnm.2833","volume":"33","author":"S Oviedo","year":"2017","unstructured":"Oviedo S, Vehi J, Calm R, Armengol J. A review of personalized blood glucose prediction strategies for T1DM patients. Int J Numer Method Biomed Eng. 2017; 33(6):1\u201321.","journal-title":"Int J Numer Method Biomed Eng"},{"key":"943_CR16","doi-asserted-by":"crossref","unstructured":"Woldaregay AZ, \u00c5rsand E, Botsis T, Albers D, Mamykina L, Hartvigsen G. Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes. J Med Internet Res; 21(5):e11030.","DOI":"10.2196\/11030"},{"key":"943_CR17","doi-asserted-by":"publisher","first-page":"931","DOI":"10.1109\/TBME.2006.889774","volume":"54","author":"G Sparacino","year":"2007","unstructured":"Sparacino G, Zanderigo F, Corazza S, Maran A, Facchinetti A, Cobelli C. Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series. IEEE Trans Biomed Eng. 2007; 54:931\u20137.","journal-title":"IEEE Trans Biomed Eng"},{"issue":"3","key":"943_CR18","doi-asserted-by":"publisher","first-page":"1251","DOI":"10.1109\/JBHI.2018.2840690","volume":"23","author":"J Yang","year":"2018","unstructured":"Yang J, Li L, Shi Y, Xie X. An ARIMA Model with Adaptive Orders for Predicting Blood Glucose Concentrations and Hypoglycemia. IEEE J Biomed Health Inform. 2018; 23(3):1251\u201360.","journal-title":"IEEE J Biomed Health Inform"},{"key":"943_CR19","doi-asserted-by":"publisher","first-page":"12329","DOI":"10.1021\/ie3034015","volume":"52","author":"K Turksoy","year":"2013","unstructured":"Turksoy K, Bayrak ES, Quinn L, Littlejohn E, Rollins D, Cinar A. Hypoglycemia early alarm systems based on multivariable models. Ind Eng Chem Res. 2013; 52:12329\u201336.","journal-title":"Ind Eng Chem Res"},{"key":"943_CR20","doi-asserted-by":"publisher","first-page":"81","DOI":"10.1089\/dia.2009.0076","volume":"12","author":"C P\u00e9rez-Gand\u00eda","year":"2010","unstructured":"P\u00e9rez-Gand\u00eda C, Facchinetti A, Sparacino G, Cobelli C, G\u00f3mez EJ, Rigla M, et al.Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. Diabetes Technol Ther. 2010; 12:81\u20138.","journal-title":"Diabetes Technol Ther"},{"key":"943_CR21","doi-asserted-by":"publisher","first-page":"1550","DOI":"10.1109\/TBME.2012.2188893","volume":"59","author":"C Zecchin","year":"2012","unstructured":"Zecchin C, Facchinetti A, Sparacino G, De Nicolao G, Cobelli C. Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration. IEEE Trans Biomed Eng. 2012; 59:1550\u201360.","journal-title":"IEEE Trans Biomed Eng"},{"key":"943_CR22","doi-asserted-by":"publisher","unstructured":"Zecchin C, Facchinetti A, Sparacino G, De Nicolao G, Cobelli C. A new neural network approach for short-term glucose prediction using continuous glucose monitoring time-series and meal information. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS: 2011. p. 5653\u20136. \n                    https:\/\/doi.org\/10.1109\/iembs.2011.6091368\n                    \n                  .","DOI":"10.1109\/iembs.2011.6091368"},{"key":"943_CR23","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.1177\/1932296816654161","volume":"10","author":"C Zecchin","year":"2016","unstructured":"Zecchin C, Facchinetti A, Sparacino G, Cobelli C. How much is short-term glucose prediction in type 1 diabetes improved by adding insulin delivery and meal content information to CGM data? A proof-of-concept study. J Diabetes Sci Technol. 2016; 10:1149\u201360.","journal-title":"J Diabetes Sci Technol"},{"key":"943_CR24","doi-asserted-by":"publisher","first-page":"135","DOI":"10.1089\/dia.2010.0104","volume":"13","author":"SM Pappada","year":"2011","unstructured":"Pappada SM, Cameron BD, Rosman PM, Bourey RE, Papadimos TJ, et al.Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. Diabetes Technol Ther. 2011; 13:135\u201341.","journal-title":"Diabetes Technol Ther"},{"key":"943_CR25","doi-asserted-by":"publisher","first-page":"1249","DOI":"10.2337\/dc09-1487","volume":"33","author":"E Dassau","year":"2010","unstructured":"Dassau E, Cameron F, Lee H, Bequette BW, Zisser H, Jovanovi\u010d L, et al.Real-Time Hypoglycemia Prediction Suite Using Continuous Glucose Monitoring. Diabetes Care. 2010; 33:1249\u201354.","journal-title":"Diabetes Care"},{"key":"943_CR26","doi-asserted-by":"publisher","unstructured":"Eljil KS, Qadah G, Pasquier M. Predicting hypoglycemia in diabetic patients using data mining techniques. 2013 9th Int Conf Innov Inf Technol. 2013:130\u20135. \n                    https:\/\/doi.org\/10.1109\/innovations.2013.6544406\n                    \n                  .","DOI":"10.1109\/innovations.2013.6544406"},{"key":"943_CR27","unstructured":"Miyeon J, et al.Prediction of Daytime Hypoglycemic Events Using Continuous Glucose Monitoring Data and Classification Technique. arXiv preprint. 2017. \n                    https:\/\/arxiv.org\/abs\/1704.08769\n                    \n                  ."},{"issue":"1","key":"943_CR28","doi-asserted-by":"publisher","first-page":"86","DOI":"10.1177\/1932296814554260","volume":"9","author":"B Sudharsan","year":"2015","unstructured":"Sudharsan B, Peeples M, Shomali M. Hypoglycemia prediction using machine learning models for patients with type 2 diabetes. J Diabetes Sci Technol. 2015; 9(1):86\u201390. \n                    https:\/\/doi.org\/10.1177\/1932296814554260\n                    \n                  .","journal-title":"J Diabetes Sci Technol"},{"key":"943_CR29","doi-asserted-by":"publisher","first-page":"500","DOI":"10.1177\/193229681300700227","volume":"7","author":"A Liebl","year":"2013","unstructured":"Liebl A, Henrichs HR, Heinemann L, Freckmann G, Biermann E, Thomas A. Continuous glucose monitoring: Evidence and consensus statement for clinical use. J Diabetes Sci Technol. 2013; 7:500\u201319.","journal-title":"J Diabetes Sci Technol"},{"issue":"5","key":"943_CR30","doi-asserted-by":"publisher","first-page":"781","DOI":"10.1089\/152091503322526987","volume":"5","author":"The Diabetes Research in Children Network (DirectNet) Study Group","year":"2003","unstructured":"The Diabetes Research in Children Network (DirectNet) Study Group. The accuracy of the CGMSTM in children with type 1 diabetes: results of the Diabetes Research in Children Network (DirecNet) accuracy study. Diabetes Technol Ther. 2003; 5(5):781\u2013789. \n                    https:\/\/doi.org\/10.1089\/152091503322526987\n                    \n                  .","journal-title":"Diabetes Technol Ther"},{"issue":"8","key":"943_CR31","doi-asserted-by":"publisher","first-page":"1340","DOI":"10.1016\/j.jdiacomp.2017.04.017","volume":"31","author":"VW Zhong","year":"2017","unstructured":"Zhong VW, Crandell JL, Shay CM, Gordon-Larsen P, Cole SR, Juhaeri J, et al.Dietary intake and risk of non-severe hypoglycemia in adolescents with type 1 diabetes. J Diabetes Complicat. 2017; 31(8):1340\u20137. \n                    https:\/\/doi.org\/10.1016\/j.jdiacomp.2017.04.017\n                    \n                  .","journal-title":"J Diabetes Complicat"},{"issue":"6","key":"943_CR32","doi-asserted-by":"publisher","first-page":"498","DOI":"10.1016\/j.diabet.2015.07.001","volume":"41","author":"M Joubert","year":"2015","unstructured":"Joubert M, Baillot-Rudoni S, Catargi B, Charpentier G, Esvant A, Franc S, et al.Indication, organization, practical implementation and interpretation guidelines for retrospective CGM recording: A French position statement. Diabetes Metab. 2015; 41(6):498\u2013508. \n                    https:\/\/doi.org\/10.1016\/j.diabet.2015.07.001\n                    \n                  .","journal-title":"Diabetes Metab"},{"key":"943_CR33","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1089\/dia.2005.7.3","volume":"7","author":"CC Palerm","year":"2005","unstructured":"Palerm CC, Willis JP, Desemone J, Bequette BW. Hypoglycemia Prediction and Detection Using Optimal Estimation. Diabetes Technol Ther. 2005; 7:3\u201314.","journal-title":"Diabetes Technol Ther"},{"issue":"Supplement 1","key":"943_CR34","doi-asserted-by":"publisher","first-page":"S55","DOI":"10.2337\/dc18-S006","volume":"41","author":"American Diabetes Association","year":"2018","unstructured":"American Diabetes Association. 6. Glycemic targets: standards of medical care in diabetes\u20142018. Diabetes Care. 2018; 41(Supplement 1):S55\u201364. \n                    https:\/\/doi.org\/10.2337\/dc18-s006\n                    \n                  .","journal-title":"Diabetes Care"},{"key":"943_CR35","doi-asserted-by":"publisher","first-page":"155","DOI":"10.2337\/dc16-2215","volume":"40","author":"International Hypoglycaemia Study Group","year":"2017","unstructured":"International Hypoglycaemia Study Group. Glucose concentrations of less than 3.0 mmol\/L (54 mg\/dL) should be reported in clinical trials: a joint position statement of the American Diabetes Association and the European Association for the Study of Diabetes. Diabetes Care. 2017; 40:155\u20137.","journal-title":"Diabetes Care"},{"issue":"1","key":"943_CR36","doi-asserted-by":"publisher","first-page":"90","DOI":"10.1111\/j.1464-5491.2006.01695.x","volume":"23","author":"HC Schaller","year":"2006","unstructured":"Schaller HC, Schaupp L, Bodenlenz M, Wilinska ME, Chassin LJ, Wach P, et al.On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: Evaluation under fasting conditions in patients with Type 1 diabetes. Diabet Med. 2006; 23(1):90\u20133. \n                    https:\/\/doi.org\/10.1111\/j.1464-5491.2006.01695.x\n                    \n                  .","journal-title":"Diabet Med"},{"issue":"2","key":"943_CR37","doi-asserted-by":"publisher","first-page":"650","DOI":"10.1109\/JBHI.2018.2823763","volume":"23","author":"M Gadaleta","year":"2018","unstructured":"Gadaleta M, Facchinetti A, Grisan E, Rossi M. Prediction of Adverse Glycemic Events from Continuous Glucose Monitoring Signal. IEEE J Biomed Health Inform. 2018; 23(2):650\u20139. \n                    https:\/\/doi.org\/10.1109\/jbhi.2018.2823763\n                    \n                  .","journal-title":"IEEE J Biomed Health Inform"},{"key":"943_CR38","first-page":"42","volume":"2","author":"V Ganganwar","year":"2012","unstructured":"Ganganwar V. An overview of classification algorithms for imbalanced datasets. J Emerg Technol Adv Eng. 2012; 2:42\u20137.","journal-title":"J Emerg Technol Adv Eng"},{"issue":"3","key":"943_CR39","doi-asserted-by":"publisher","first-page":"145","DOI":"10.4103\/picr.picr_87_18","volume":"9","author":"AK Akobeng","year":"2007","unstructured":"Akobeng AK. Understanding diagnostic tests 3: Receiver operating characteristic curves. Acta Paediatr Int J Paediatr. 2007; 9(3):145. \n                    https:\/\/doi.org\/10.4103\/picr.picr_87_18\n                    \n                  .","journal-title":"Acta Paediatr Int J Paediatr"},{"key":"943_CR40","unstructured":"Plis K, Bunescu R, Marling C, Shubrook J, Schwartz F. A Machine Learning Approach to Predicting Blood Glucose Levels for Diabetes Management. In: Workshops at the Twenty-Eighth AAAI Conference on Artific ial Intelligence: 2014: 2014."},{"key":"943_CR41","unstructured":"Longadge R, Dongre S, Malik L. Class imbalance problem in data mining review. 2013. arXiv preprint arXiv:13051707."},{"issue":"1","key":"943_CR42","doi-asserted-by":"publisher","first-page":"624","DOI":"10.1177\/193229680700100505","volume":"5","author":"CC Palerm","year":"2007","unstructured":"Palerm CC, Willis JP, Desemone J, Bequette BW. Hypoglycemia detection and prediction using continuous glucose monitoring - A study on hypoglycemic clamp data. J Diabetes Sci Technol. 2007; 5(1):624\u20139. \n                    https:\/\/doi.org\/10.1177\/193229680700100505\n                    \n                  .","journal-title":"J Diabetes Sci Technol"},{"key":"943_CR43","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3389\/fams.2017.00014","volume":"3","author":"HN Mhaskar","year":"2017","unstructured":"Mhaskar HN, Pereverzyev SV, van der Walt MD. A deep learning approach to diabetic blood glucose prediction. Front Appl Math Stat. 2017; 3:14. \n                    https:\/\/doi.org\/10.3389\/fams.2017.00014\n                    \n                  .","journal-title":"Front Appl Math Stat"},{"key":"943_CR44","doi-asserted-by":"publisher","first-page":"2072","DOI":"10.2337\/dc08-1441","volume":"31","author":"LC Perlmuter","year":"2008","unstructured":"Perlmuter LC, Flanagan BP, Shah PH, Singh SP. Glycemic Control and Hypoglycemia: Is the loser the winner?Diabetes Care. 2008; 31:2072\u20136.","journal-title":"Diabetes Care"},{"issue":"5","key":"943_CR45","doi-asserted-by":"publisher","first-page":"947","DOI":"10.1177\/1932296815603528","volume":"9","author":"L Heinemann","year":"2015","unstructured":"Heinemann L, Freckmann G. CGM versus FGM; or, continuous glucose monitoring is not flash glucose monitoring. J Diabetes Sci Technol. 2015; 9(5):947\u201350. \n                    https:\/\/doi.org\/10.1177\/1932296815603528\n                    \n                  .","journal-title":"J Diabetes Sci Technol"}],"container-title":["BMC Medical Informatics and Decision Making"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0943-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/article\/10.1186\/s12911-019-0943-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1186\/s12911-019-0943-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,11,5]],"date-time":"2020-11-05T00:23:11Z","timestamp":1604535791000},"score":1,"resource":{"primary":{"URL":"https:\/\/bmcmedinformdecismak.biomedcentral.com\/articles\/10.1186\/s12911-019-0943-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,11,6]]},"references-count":45,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2019,12]]}},"alternative-id":["943"],"URL":"https:\/\/doi.org\/10.1186\/s12911-019-0943-4","relation":{},"ISSN":["1472-6947"],"issn-type":[{"value":"1472-6947","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,11,6]]},"assertion":[{"value":"12 February 2019","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 October 2019","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 November 2019","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The protocol of this study was approved by the Institutional Review Board (IRB) of the Samsung Medical Center. The informed consent requirement for this study was waived because the researcher only accessed the database for analysis purposes, and all patient data were de-identified (IRB file number; SMC 2016-05-058-001) All medical procedures were performed in accordance with relevant guidelines and regulations. The informed consent requirement for this study was waived by the board because the researcher only accessed the database for analysis purposes, and all patient data were de-identified.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval and consent to participate"}},{"value":"Not applicable.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}},{"value":"The authors declare that they have no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"210"}}