{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T12:54:45Z","timestamp":1791982485426,"version":"4.4.0"},"reference-count":34,"publisher":"SAGE Publications","issue":"6","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["J Diabetes Sci Technol"],"published-print":{"date-parts":[[2023,11]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>In this work, we leverage state-of-the-art deep learning\u2013based algorithms for blood glucose (BG) forecasting in people with type 1 diabetes.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>We propose stacks of convolutional neural network and long short-term memory units to predict BG level for 30-, 60-, and 90-minute prediction horizon (PH), given historical glucose measurements, meal information, and insulin intakes. The evaluation was performed on two data sets, Replace-BG and DIAdvisor, representative of free-living conditions and in-hospital setting, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>For 90-minute PH, our model obtained mean absolute error of 17.30 \u00b1 2.07 and 18.23 \u00b1 2.97 mg\/dL, root mean square error of 23.45 \u00b1 3.18 and 25.12 \u00b1 4.65 mg\/dL, coefficient of determination of 84.13 \u00b1 4.22% and 82.34 \u00b1 4.54%, and in terms of the continuous glucose-error grid analysis 94.71 \u00b1 3.89% and 91.71 \u00b1 4.32% accurate predictions, 1.81 \u00b1 1.06% and 2.51 \u00b1 0.86% benign errors, and 3.47 \u00b1 1.12% and 5.78 \u00b1 1.72% erroneous predictions, for Replace-BG and DIAdvisor data sets, respectively.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>Our investigation demonstrated that our method achieved superior glucose forecasting compared with existing approaches in the literature, and thanks to its generalizability showed potential for real-life applications.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/19322968221092785","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T07:26:48Z","timestamp":1650871608000},"page":"1590-1601","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":81,"title":["Long-Term Prediction of Blood Glucose Levels in Type 1 Diabetes Using a CNN-LSTM-Based Deep Neural Network"],"prefix":"10.1177","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2417-9263","authenticated-orcid":false,"given":"Mehrad","family":"Jaloli","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, University of Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marzia","family":"Cescon","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, University of Houston, Houston, TX, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25913-0"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.3390\/s19204482"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.1109\/TBME.2006.889774"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2009.2034141"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.1089\/dia.2008.0065"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1177\/193229680900300526"},{"key":"e_1_3_2_8_2","first-page":"5164","volume-title":"2011 50th IEEE Conference on Decision and Control and European Control Conference","author":"Cescon M","unstructured":"Cescon M, Renard E. Adaptive Subspace-based prediction of T1DM glycemia. In: 2011 50th IEEE Conference on Decision and Control and European Control Conference, Orlando, Florida, 12-15 December 2011, IEEE; 5164-5169"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.3182\/20090706-3-FR-2004.00039"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-25913-0_7"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2014.09.012"},{"key":"e_1_3_2_12_2","first-page":"A11","article-title":"Using machine learning to predict glucose changes during aerobic, anaerobic and mixed forms of exercise in patients with type 1 diabetes","volume":"22","author":"Jacobs P","year":"2020","unstructured":"Jacobs P, Young G, Tyler N, et al. Using machine learning to predict glucose changes during aerobic, anaerobic and mixed forms of exercise in patients with type 1 diabetes. Diabetes Tech Therapeut. 2020;22:A11\u2013A11.","journal-title":"Diabetes Tech Therapeut"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.bbe.2020.10.004"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.3389\/fgene.2018.00515"},{"key":"e_1_3_2_15_2","doi-asserted-by":"publisher","DOI":"10.1007\/s41666-019-00063-2"},{"key":"e_1_3_2_16_2","first-page":"135","volume-title":"2013 12th International Conference on Machine Learning and Applications (ICMLA\u201913)","volume":"1","author":"Bunescu R","unstructured":"Bunescu R, Struble N, Marling C, Shubrook J, Schwartz F. Blood glucose level prediction using physiological models and support vector regression. In 2013 12th International Conference on Machine Learning and Applications (ICMLA\u201913) Miami, Florida, vol. 1, 4-7 December, 2013:135-140. IEEE."},{"key":"e_1_3_2_17_2","first-page":"27","volume-title":"Workshops at the Twenty-eighth AAAI Conference on Artificial Intelligence, Quebec, Canada","author":"Plis K","year":"2014","unstructured":"Plis K, Bunescu R, Marling C, Shubrook J, Schwartz F. A machine learning approach to predicting blood glucose levels for diabetes management. Workshops at the Twenty-eighth AAAI Conference on Artificial Intelligence, Quebec, Canada, 27-28 July 2014."},{"key":"e_1_3_2_18_2","doi-asserted-by":"crossref","DOI":"10.3390\/app11041742","article-title":"A comparison of feature selection and forecasting machine learning algorithms for predicting glycaemia in type 1 diabetes mellitus","volume":"11","author":"Rodr\u00edguez-Rodr\u00edguez I","year":"2021","unstructured":"Rodr\u00edguez-Rodr\u00edguez I, Rodr\u00edguez J-V, Woo WL, Wei B, Pardo-Quiles D-J. A comparison of feature selection and forecasting machine learning algorithms for predicting glycaemia in type 1 diabetes mellitus. Applied Sciences. 2021;11:1742","journal-title":"Applied Sciences"},{"key":"e_1_3_2_19_2","first-page":"64","volume-title":"3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI)","author":"Zhu T","unstructured":"Zhu T, Li K, Herrero P, Chen J, Georgiou P. A deep learning algorithm for personalized blood glucose prediction, 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence (IJCAI-ECAI), Stockholm, Sweden, 13-19 July, 2018: 64-78."},{"key":"e_1_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2931842"},{"key":"e_1_3_2_21_2","first-page":"2887","volume-title":"39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)","author":"Mirshekarian S","unstructured":"Mirshekarian S, Bunescu R, Marling C, Schwartz F. Using LSTMs to learn physiological models of blood glucose behavior. 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Jeju Island, Korea, 11-15 July, 2017: 2887-2891. IEEE."},{"key":"e_1_3_2_22_2","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2019.2908488"},{"key":"e_1_3_2_23_2","unstructured":"Mehrad Jaloli Marzia Cescon. Predicting Blood Glucose Levels Using CNN-LSTM Neural Networks. 2020 Diabetes Technology Meeting Abstracts Vol. 15. 2nd ed. Los Angeles CA: Sage; 2020."},{"key":"e_1_3_2_24_2","doi-asserted-by":"publisher","DOI":"10.2337\/dc16-2482"},{"key":"e_1_3_2_25_2","volume-title":"Modeling and Prediction in Diabetes Physiology","author":"Cescon M.","year":"2013","unstructured":"Cescon M. Modeling and Prediction in Diabetes Physiology. Department of Automatic Control, Lund University, Lund, Sweden; 2013 Nov."},{"key":"e_1_3_2_26_2","doi-asserted-by":"publisher","DOI":"10.3390\/s20061705"},{"key":"e_1_3_2_27_2","first-page":"69","volume-title":"3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence","author":"Chen J","unstructured":"Chen J, Li K, Herrero P, Zhu T, Georgiou P. Dilated recurrent neural network for short-time prediction of glucose concentration, 3rd International Workshop on Knowledge Discovery in Healthcare Data co-located with the 27th International Joint Conference on Artificial Intelligence and the 23rd European Conference on Artificial Intelligence, Stockholm, Sweden, 13-19 July 2018: pp. 69-73"},{"key":"e_1_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1109\/23.589532"},{"key":"e_1_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ins.2011.12.028"},{"key":"e_1_3_2_30_2","first-page":"265","volume-title":"12th {USENIX} symposium on operating systems design and implementation (OSDI 16)","volume":"2016","author":"Abadi M","unstructured":"Abadi M, Barham P, Chen J, et al. Tensorflow: a system for large-scale machine learning. 12th {USENIX} symposium on operating systems design and implementation (OSDI 16), Savannah, GA, 2-4 November, 2016: 265-283"},{"key":"e_1_3_2_31_2","doi-asserted-by":"publisher","DOI":"10.2337\/diacare.27.8.1922"},{"key":"e_1_3_2_32_2","doi-asserted-by":"publisher","DOI":"10.1002\/cnm.2833"},{"key":"e_1_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.1109\/TITB.2012.2219876"},{"key":"e_1_3_2_34_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-021-02437-4"},{"key":"e_1_3_2_35_2","first-page":"110","volume-title":"Paper presented at the KDH@ ECAI, Santiago de Compostela","author":"Daniels J","unstructured":"Daniels J, Herrero P, Georgiou P. Personalised glucose prediction via deep multitask networks. Paper presented at the KDH@ ECAI, Santiago de Compostela, Spain, 29-30 August 2020: 110-114"}],"container-title":["Journal of Diabetes Science and Technology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968221092785","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/19322968221092785","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/19322968221092785","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,28]],"date-time":"2026-04-28T18:15:53Z","timestamp":1777400153000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/19322968221092785"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":34,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,11]]}},"alternative-id":["10.1177\/19322968221092785"],"URL":"https:\/\/doi.org\/10.1177\/19322968221092785","relation":{},"ISSN":["1932-2968","1932-2968"],"issn-type":[{"value":"1932-2968","type":"print"},{"value":"1932-2968","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,4,25]]}}}