{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T13:02:30Z","timestamp":1780664550812,"version":"3.54.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Int. J. Mach. Learn. &amp; Cyber."],"published-print":{"date-parts":[[2026,5]]},"DOI":"10.1007\/s13042-026-03079-4","type":"journal-article","created":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T05:27:25Z","timestamp":1774934845000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Dual hormone controller for type 1 diabetes based on dueling deep Q-network and insulin action replay"],"prefix":"10.1007","volume":"17","author":[{"given":"Namit","family":"Arora","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gorseet Paul","family":"Singh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bhomic","family":"Kaushik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheetal","family":"Rajpal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Virendra","family":"Kumar","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ankit","family":"Rajpal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"issue":"1","key":"3079_CR1","first-page":"414","volume":"771","author":"V Valla","year":"2012","unstructured":"Valla V (2012) Continuous subcutaneous insulin infusion (CSII) pumps. Adv Exp Med Biol 771(1):414\u2013419","journal-title":"Adv Exp Med Biol"},{"key":"3079_CR2","doi-asserted-by":"publisher","first-page":"2633","DOI":"10.1109\/JBHI.2018.2887067","volume":"23","author":"Q Sun","year":"2018","unstructured":"Sun Q, Jankovic MV, Budzinski J, Moore B, Diem P, Stettler C, Mougiakakou SG (2018) A dual mode adaptive basal-bolus advisor based on reinforcement learning. IEEE J Biomed Health Inform 23:2633\u20132641","journal-title":"IEEE J Biomed Health Inform"},{"issue":"7540","key":"3079_CR3","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1038\/nature14236","volume":"518","author":"V Mnih","year":"2015","unstructured":"Mnih V, Kavukcuoglu K, Silver D, Rusu AA, Veness J, Bellemare MG, Graves A, Riedmiller M, Fidjeland AK, Ostrovski G, Petersen S, Beattie C, Sadik A, Antonoglou I, King H, Kumaran D, Wierstra D, Legg S, Hassabis D (2015) Human-level control through deep reinforcement learning. Nature 518(7540):529\u201333","journal-title":"Nature"},{"key":"3079_CR4","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2023-074984","volume":"13","author":"M Jancev","year":"2023","unstructured":"Jancev M, Snoek F, Frederix G, Knottnerus H, Blauw H, Witkop M, Moons K, Bon A, DeVries J, Sern\u00e9 E, Sloten T, de Valk H (2023) Dual hormone fully closed loop in type 1 diabetes: a randomised trial in the Netherlands-study protocol. BMJ Open 13:e074984","journal-title":"BMJ Open"},{"key":"3079_CR5","doi-asserted-by":"crossref","unstructured":"Kedia N (2011) Treatment of severe diabetic hypoglycemia with glucagon: an underutilized therapeutic approach. In: Diabetes, metabolic syndrome and obesity: targets and therapy, pp 337\u2013346","DOI":"10.2147\/DMSO.S20633"},{"issue":"6","key":"3079_CR6","doi-asserted-by":"publisher","first-page":"6927","DOI":"10.1007\/s40747-023-01100-9","volume":"9","author":"K Zaman","year":"2023","unstructured":"Zaman K, Zhaoyun S, Shah B, Hussain T, Shah SM, Ali F, Khan US (2023) A novel driver emotion recognition system based on deep ensemble classification. Complex Int Syst 9(6):6927\u20136952","journal-title":"Complex Int Syst"},{"issue":"1","key":"3079_CR7","doi-asserted-by":"publisher","first-page":"6611276","DOI":"10.1155\/int\/6611276","volume":"2025","author":"K Zaman","year":"2025","unstructured":"Zaman K, Zengkang G, Zhaoyun S, Shah SM, Riaz W, Ji J, Hussain T, Attar RW (2025) A novel emotion recognition system for human-robot interaction (HRI) using deep ensemble classification. Int J Intell Syst 2025(1):6611276","journal-title":"Int J Intell Syst"},{"issue":"12","key":"3079_CR8","doi-asserted-by":"publisher","first-page":"9477","DOI":"10.1109\/TPAMI.2021.3127674","volume":"44","author":"O Tutsoy","year":"2021","unstructured":"Tutsoy O (2021) Pharmacological, non-pharmacological policies and mutation: an artificial intelligence based multi-dimensional policy making algorithm for controlling the casualties of the pandemic diseases. IEEE Trans Pattern Anal Mach Intell 44(12):9477\u20139488","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"issue":"4","key":"3079_CR9","doi-asserted-by":"publisher","first-page":"bbae344","DOI":"10.1093\/bib\/bbae344","volume":"25","author":"O Tutsoy","year":"2024","unstructured":"Tutsoy O, Sumbul HE (2024) A novel deep machine learning algorithm with dimensionality and size reduction approaches for feature elimination: thyroid cancer diagnoses with randomly missing data. Brief Bioinform 25(4):bbae344","journal-title":"Brief Bioinform"},{"key":"3079_CR10","doi-asserted-by":"publisher","first-page":"1191","DOI":"10.2337\/dc13-2108","volume":"37","author":"F Doyle","year":"2014","unstructured":"Doyle F, Huyett L, Lee J, Zisser H, Dassau E (2014) Closed-loop artificial pancreas systems: engineering the algorithms. Diabetes Care 37:1191\u20137","journal-title":"Diabetes Care"},{"issue":"11","key":"3079_CR11","doi-asserted-by":"publisher","first-page":"664","DOI":"10.3390\/bioengineering9110664","volume":"9","author":"JLCB de Farias","year":"2022","unstructured":"de Farias JLCB, Bessa WM (2022) Intelligent control with artificial neural networks for automated insulin delivery systems. Bioengineering 9(11):664","journal-title":"Bioengineering"},{"key":"3079_CR12","first-page":"1","volume":"PP","author":"T Zhu","year":"2020","unstructured":"Zhu T, Li K, Herrero P, Georgiou P (2020) Deep learning for diabetes: a systematic review. IEEE J Biomed Health Inform PP:1\u20131","journal-title":"IEEE J Biomed Health Inform"},{"key":"3079_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.120156","volume":"228","author":"X Yu","year":"2023","unstructured":"Yu X, Guan Y, Yan L, Li S, Fu X, Jiang J (2023) ARLPE: a meta reinforcement learning framework for glucose regulation in type 1 diabetics. Expert Syst Appl 228:120156","journal-title":"Expert Syst Appl"},{"key":"3079_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102749","volume":"148","author":"A Jafar","year":"2024","unstructured":"Jafar A, Pasqua M-R, Olson B, Haidar A (2024) Advanced decision support system for individuals with diabetes on multiple daily injections therapy using reinforcement learning and nearest-neighbors: In-silico and clinical results. Artif Intell Med 148:102749","journal-title":"Artif Intell Med"},{"key":"3079_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2023.104376","volume":"142","author":"H Emerson","year":"2023","unstructured":"Emerson H, Guy M, McConville R (2023) Offline reinforcement learning for safer blood glucose control in people with type 1 diabetes. J Biomed Inform 142:104376","journal-title":"J Biomed Inform"},{"key":"3079_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2023.105839","volume":"90","author":"C Hettiarachchi","year":"2024","unstructured":"Hettiarachchi C, Malagutti N, Nolan CJ, Suominen H, Daskalaki E (2024) G2P2C\u2014a modular reinforcement learning algorithm for glucose control by glucose prediction and planning in Type 1 Diabetes. Biomed Signal Process Control 90:105839","journal-title":"Biomed Signal Process Control"},{"issue":"8","key":"3079_CR17","doi-asserted-by":"publisher","first-page":"564","DOI":"10.1089\/dia.2021.0566","volume":"24","author":"R Nimri","year":"2022","unstructured":"Nimri R, Tirosh A, Muller I, Shtrit Y, Kraljevic I, Alonso MM, Milicic T, Saboo B, Deeb A, Christoforidis A et al (2022) Comparison of insulin dose adjustments made by artificial intelligence-based decision support systems and by physicians in people with type 1 diabetes using multiple daily injections therapy. Diabetes Technology & Therapeutics 24(8):564\u2013572","journal-title":"Diabetes Technology & Therapeutics"},{"issue":"4","key":"3079_CR18","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1109\/JBHI.2020.3014556","volume":"25","author":"T Zhu","year":"2021","unstructured":"Zhu T, Li K, Herrero P, Georgiou P (2021) Basal glucose control in type 1 diabetes using deep reinforcement learning: an in silico validation. IEEE J Biomed Health Inform 25(4):1223\u20131232","journal-title":"IEEE J Biomed Health Inform"},{"issue":"5","key":"3079_CR19","doi-asserted-by":"publisher","first-page":"87","DOI":"10.3390\/ai6050087","volume":"6","author":"X Yu","year":"2025","unstructured":"Yu X, Yang Z, Sun X, Liu H, Li H, Lu J, Zhou J, Cinar A (2025) Deep reinforcement learning for automated insulin delivery systems: algorithms, applications and prospects. AI 6(5):87","journal-title":"AI"},{"key":"3079_CR20","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1177\/1932296813514502","volume":"8","author":"CD Man","year":"2014","unstructured":"Man CD, Micheletto F, Lv D, Breton M, Kovatchev B, Cobelli C (2014) The UVA\/PADOVA Type 1 diabetes simulator. J Diabetes Sci Technol 8:26\u201334","journal-title":"J Diabetes Sci Technol"},{"key":"3079_CR21","first-page":"279","volume":"8","author":"C Watkins","year":"1992","unstructured":"Watkins C, Dayan P (1992) Technical note: Q-learning. Mach Learn 8:279\u2013292","journal-title":"Mach Learn"},{"key":"3079_CR22","doi-asserted-by":"publisher","first-page":"jc579","DOI":"10.1210\/jc.2014-1579","volume":"99","author":"J Yoshino","year":"2014","unstructured":"Yoshino J, Almeda-Valdes P, Patterson B, Okunade A, Imai S-I, Mittendorfer B, Klein S (2014) Diurnal variation in insulin sensitivity of glucose metabolism is associated with diurnal variations in whole-body and cellular fatty acid metabolism in metabolically normal women. J Clin Endocrinol Metab 99:jc579","journal-title":"J Clin Endocrinol Metab"},{"key":"3079_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1080\/10255842.2017.1378352","volume":"20","author":"P Herrero","year":"2017","unstructured":"Herrero P, Bondia J, Oliver N, Georgiou P (2017) A coordinated control strategy for insulin and glucagon delivery in type 1 diabetes. Comput Methods Biomech Biomed Eng 20:1\u20139","journal-title":"Comput Methods Biomech Biomed Eng"},{"issue":"6","key":"3079_CR24","doi-asserted-by":"publisher","first-page":"836","DOI":"10.4103\/ijem.IJEM_226_17","volume":"21","author":"OJ Lakhani","year":"2017","unstructured":"Lakhani OJ, Kumar S, Tripathi S, Desai M, Seth C (2017) Comparison of two protocols in the management of glucocorticoid-induced hyperglycemia among hospitalized patients. Indian J Endocrinol Metab 21(6):836\u2013844","journal-title":"Indian J Endocrinol Metab"},{"issue":"7","key":"3079_CR25","doi-asserted-by":"publisher","first-page":"1335","DOI":"10.2337\/dc09-9032","volume":"32","author":"AE Kitabchi","year":"2009","unstructured":"Kitabchi AE, Umpierrez GE, Miles JM, Fisher JN (2009) Hyperglycemic crises in adult patients with diabetes. Diabetes Care 32(7):1335","journal-title":"Diabetes Care"},{"key":"3079_CR26","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1007\/s41666-020-00068-2","volume":"4","author":"T Zhu","year":"2020","unstructured":"Zhu T, Li K, Chen J, Herrero P, Georgiou P (2020) Dilated recurrent neural networks for glucose forecasting in type 1 diabetes. J Healthc Inform Res 4:308\u2013324","journal-title":"J Healthc Inform Res"},{"key":"3079_CR27","unstructured":"OpenAPS (2017) Understanding Insulin on Board (IOB) Calculations. https:\/\/openaps.readthedocs.io\/en\/latest\/docs\/While%20You%20Wait%20For%20Gear\/understanding-insulin-on-board-calculations.html. Accessed 15 May 2024"},{"key":"3079_CR28","doi-asserted-by":"publisher","first-page":"641","DOI":"10.2165\/00003088-200140090-00002","volume":"40","author":"A Lindholm","year":"2001","unstructured":"Lindholm A, Jacobsen LV (2001) Clinical pharmacokinetics and pharmacodynamics of insulin aspart. Clin Pharmacokinet 40:641\u2013659","journal-title":"Clin Pharmacokinet"},{"key":"3079_CR29","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.2165\/00003088-200241130-00003","volume":"41","author":"P Roach","year":"2002","unstructured":"Roach P, Woodworth JR (2002) Clinical pharmacokinetics and pharmacodynamics of insulin lispro mixtures. Clin Pharmacokinet 41:1043\u20131057","journal-title":"Clin Pharmacokinet"},{"issue":"7","key":"3079_CR30","doi-asserted-by":"publisher","first-page":"432","DOI":"10.1016\/j.tem.2019.04.008","volume":"30","author":"B Kovatchev","year":"2019","unstructured":"Kovatchev B (2019) A century of diabetes technology: signals, models, and artificial pancreas control. Trends Endocrinol Metab 30(7):432\u2013444","journal-title":"Trends Endocrinol Metab"},{"key":"3079_CR31","unstructured":"Hasselt Hv, Guez A, Silver D (2016) Deep reinforcement learning with double Q-Learning. In: Proceedings of the thirtieth AAAI conference on artificial intelligence, pp\u00a02094\u20132100. AAAI Press"},{"key":"3079_CR32","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","volume":"20","author":"PJ Rousseeuw","year":"1987","unstructured":"Rousseeuw PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J Comput Appl Math 20:53\u201365","journal-title":"J Comput Appl Math"},{"key":"3079_CR33","doi-asserted-by":"crossref","unstructured":"Shahapure KR, Nicholas C (2020) Cluster quality analysis using Silhouette score. In: 2020 IEEE 7th international conference on data science and advanced analytics (DSAA), pp\u00a0747\u2013748. IEEE","DOI":"10.1109\/DSAA49011.2020.00096"},{"key":"3079_CR34","unstructured":"Chang S, Zhang Y, Han W, Yu M, Guo X, Tan W, Cui X, Witbrock M, Hasegawa-Johnson MA, Huang TS (2017) Dilated recurrent neural networks. In: Advances in neural information processing systems, vol.\u00a030"},{"issue":"4","key":"3079_CR35","doi-asserted-by":"publisher","first-page":"928","DOI":"10.1177\/193229681300700415","volume":"7","author":"C Toffanin","year":"2013","unstructured":"Toffanin C, Zisser H, Doyle FJ III, Dassau E (2013) Dynamic insulin on board: incorporation of circadian insulin sensitivity variation. J Diabetes Sci Technol 7(4):928\u2013940","journal-title":"J Diabetes Sci Technol"},{"key":"3079_CR36","doi-asserted-by":"crossref","unstructured":"Shah VN, Rewers A, Garg S (2020) Low glucose suspend systems. In: Glucose monitoring devices, pp\u00a0257\u2013274. Elsevier","DOI":"10.1016\/B978-0-12-816714-4.00013-2"},{"issue":"5","key":"3079_CR37","doi-asserted-by":"publisher","first-page":"288","DOI":"10.1089\/dia.2016.0319","volume":"19","author":"BA Buckingham","year":"2017","unstructured":"Buckingham BA, Bailey TS, Christiansen M, Garg S, Weinzimer S, Bode B, Anderson SM, Brazg R, Ly TT, Kaufman FR (2017) Evaluation of a predictive low-glucose management system in-clinic. Diabetes Technol Ther 19(5):288\u2013292","journal-title":"Diabetes Technol Ther"},{"key":"3079_CR38","doi-asserted-by":"crossref","unstructured":"Clarke W, Kovatchev B (2009) Statistical tools to analyze continuous glucose monitor data. Diabetes Technol Ther 11(S1):S\u201345","DOI":"10.1089\/dia.2008.0138"},{"issue":"1","key":"3079_CR39","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1038\/s43856-024-00476-0","volume":"4","author":"O Mujahid","year":"2024","unstructured":"Mujahid O, Contreras I, Beneyto A, Vehi J (2024) Generative deep learning for the development of a type 1 diabetes simulator. Commun Med 4(1):51","journal-title":"Commun Med"},{"key":"3079_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2025.110147","volume":"191","author":"A Marchetti","year":"2025","unstructured":"Marchetti A, Sasso D, D\u2019Antoni F, Morandin F, Parton M, Matarrese MAG, Merone M (2025) Deep reinforcement learning for type 1 diabetes: dual PPO controller for personalized insulin management. Comput Biol Med 191:110147","journal-title":"Comput Biol Med"}],"container-title":["International Journal of Machine Learning and Cybernetics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03079-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s13042-026-03079-4","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s13042-026-03079-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T12:07:35Z","timestamp":1780661255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s13042-026-03079-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":40,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,5]]}},"alternative-id":["3079"],"URL":"https:\/\/doi.org\/10.1007\/s13042-026-03079-4","relation":{},"ISSN":["1868-8071","1868-808X"],"issn-type":[{"value":"1868-8071","type":"print"},{"value":"1868-808X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]},"assertion":[{"value":"7 September 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 March 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2026","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This article does not contain any studies with human participants or animals performed by any authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Informed consent was obtained from all participants in the study.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Informed consent"}}],"article-number":"244"}}