{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T23:19:47Z","timestamp":1774048787529,"version":"3.50.1"},"reference-count":49,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100016808","name":"Natural Science Foundation of Xiamen Municipality","doi-asserted-by":"publisher","award":["3502Z202573063"],"award-info":[{"award-number":["3502Z202573063"]}],"id":[{"id":"10.13039\/100016808","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100009013","name":"Jimei University","doi-asserted-by":"publisher","award":["ZQ2024072"],"award-info":[{"award-number":["ZQ2024072"]}],"id":[{"id":"10.13039\/501100009013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003392","name":"Natural Science Foundation of Fujian Province","doi-asserted-by":"publisher","award":["2022J01336"],"award-info":[{"award-number":["2022J01336"]}],"id":[{"id":"10.13039\/501100003392","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Biomedical Informatics"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1016\/j.jbi.2026.104998","type":"journal-article","created":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T16:16:00Z","timestamp":1770826560000},"page":"104998","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["AFTS: A patient-agnostic encoder\u2013decoder architecture with directional attention for blood glucose forecasting"],"prefix":"10.1016","volume":"176","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5806-3967","authenticated-orcid":false,"given":"Yu","family":"Chen","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0008-5875-0724","authenticated-orcid":false,"given":"Henghong","family":"Lin","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7962-2827","authenticated-orcid":false,"given":"Zhijin","family":"Wang","sequence":"additional","affiliation":[]},{"given":"Jinmo","family":"Tang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3437-1120","authenticated-orcid":false,"given":"Yaohui","family":"Huang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5133-6688","authenticated-orcid":false,"given":"Xiufeng","family":"Liu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8949-6876","authenticated-orcid":false,"given":"Senzhen","family":"Wu","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.jbi.2026.104998_b1","article-title":"IDF diabetes atlas, 10th edition","author":"Federation","year":"2021","journal-title":"Int. Diabetes Fed."},{"key":"10.1016\/j.jbi.2026.104998_b2","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jbi.2017.09.013","article-title":"Personal discovery in diabetes self-management: Discovering cause and effect using self-monitoring data","volume":"76","author":"Mamykina","year":"2017","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2026.104998_b3","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2021.102120","article-title":"Data-based algorithms and models using diabetics real data for blood glucose and hypoglycaemia prediction\u2013a systematic literature review","volume":"118","author":"Felizardo","year":"2021","journal-title":"Artif. Intell. Med."},{"issue":"9","key":"10.1016\/j.jbi.2026.104998_b4","doi-asserted-by":"crossref","first-page":"1814","DOI":"10.2337\/dc12-0749","article-title":"Severe hypoglycemia predicts mortality in diabetes","volume":"35","author":"Cryer","year":"2012","journal-title":"Diabetes Care"},{"key":"10.1016\/j.jbi.2026.104998_b5","doi-asserted-by":"crossref","unstructured":"N.A. Mahdy, A.F. Elnokrashy, D.A. Hammad, W.A. Mohamed, Intelligent Real-Time Hypoglycemia Prediction for Type 1 Diabetes, in: Proceedings of the 2024 Intelligent Methods, Systems, and Applications, IMSA, Cairo, Egypt, 2024, pp. 325\u2013329, http:\/\/dx.doi.org\/10.1109\/IMSA61967.2024.10652799.","DOI":"10.1109\/IMSA61967.2024.10652799"},{"key":"10.1016\/j.jbi.2026.104998_b6","doi-asserted-by":"crossref","DOI":"10.1016\/j.bios.2021.113054","article-title":"Continuous glucose monitoring systems-current status and future perspectives of the flagship technologies in biosensor research","volume":"181","author":"Lee","year":"2021","journal-title":"Biosens. Bioelectron."},{"issue":"1","key":"10.1016\/j.jbi.2026.104998_b7","doi-asserted-by":"crossref","first-page":"129","DOI":"10.3390\/diabetology5010010","article-title":"Utility of flash glucose monitoring to determine glucose variation induced by different doughs in persons with type 2 diabetes","volume":"5","author":"Taras","year":"2024","journal-title":"Diabetology"},{"issue":"8","key":"10.1016\/j.jbi.2026.104998_b8","doi-asserted-by":"crossref","first-page":"1593","DOI":"10.2337\/dci19-0028","article-title":"Clinical targets for continuous glucose monitoring data interpretation: recommendations from the international consensus on time in range","volume":"42","author":"Battelino","year":"2019","journal-title":"Diabetes Care"},{"issue":"1","key":"10.1016\/j.jbi.2026.104998_b9","doi-asserted-by":"crossref","first-page":"175","DOI":"10.3390\/make5010013","article-title":"Machine learning and prediction of infectious diseases: A systematic review","volume":"5","author":"Santangelo","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"issue":"1","key":"10.1016\/j.jbi.2026.104998_b10","doi-asserted-by":"crossref","first-page":"169","DOI":"10.3390\/make5010010","article-title":"Explainable machine learning","volume":"5","author":"Garcke","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.jbi.2026.104998_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2023.102659","article-title":"An interpretable deep learning model for time-series electronic health records: Case study of delirium prediction in critical care","volume":"144","author":"Sheikhalishahi","year":"2023","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.jbi.2026.104998_b12","series-title":"Proceedings of the 32nd International Conference on Neural Information Processing Systems","first-page":"1603","article-title":"Multivariate time series imputation with generative adversarial networks","author":"Luo","year":"2018"},{"key":"10.1016\/j.jbi.2026.104998_b13","series-title":"Proceedings of the 2021 IEEE International Conference on Acoustics, Speech and Signal Processing","first-page":"1240","article-title":"Hierarchical attention-based temporal convolutional networks for eeg-based emotion recognition","author":"Li","year":"2021"},{"issue":"2","key":"10.1016\/j.jbi.2026.104998_b14","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.artmed.2011.05.001","article-title":"Non-invasive estimate of blood glucose and blood pressure from a photoplethysmograph by means of machine learning techniques","volume":"53","author":"Monte-Moreno","year":"2011","journal-title":"Artif. Intell. Med."},{"key":"10.1016\/j.jbi.2026.104998_b15","series-title":"Proceedings of the 24th International Conference on Knowledge Discovery & Data Mining","first-page":"1387","article-title":"Deep multi-output forecasting: Learning to accurately predict blood glucose trajectories","author":"Fox","year":"2018"},{"issue":"1","key":"10.1016\/j.jbi.2026.104998_b16","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.jsse.2025.02.003","article-title":"Autonomous flight termination system: A proposal for an international regulatory frame and set of requirements","volume":"12","author":"Pasciuti","year":"2025","journal-title":"J. Space Saf. Eng."},{"key":"10.1016\/j.jbi.2026.104998_b17","doi-asserted-by":"crossref","DOI":"10.1016\/j.artmed.2020.101836","article-title":"Reinforcement learning application in diabetes blood glucose control: A systematic review","volume":"104","author":"Tejedor","year":"2020","journal-title":"Artif. Intell. Med.","ISSN":"https:\/\/id.crossref.org\/issn\/0933-3657","issn-type":"print"},{"key":"10.1016\/j.jbi.2026.104998_b18","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.csbj.2016.12.005","article-title":"Machine learning and data mining methods in diabetes research","volume":"15","author":"Kavakiotis","year":"2017","journal-title":"Comput. Struct. Biotechnol. J."},{"key":"10.1016\/j.jbi.2026.104998_b19","doi-asserted-by":"crossref","first-page":"308","DOI":"10.1007\/s41666-020-00068-2","article-title":"Dilated recurrent neural networks for glucose forecasting in type 1 diabetes","volume":"4","author":"Zhu","year":"2020","journal-title":"J. Heal. Inform. Res."},{"issue":"2","key":"10.1016\/j.jbi.2026.104998_b20","doi-asserted-by":"crossref","first-page":"414","DOI":"10.1109\/JBHI.2019.2931842","article-title":"GluNet: A deep learning framework for accurate glucose forecasting","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"10.1016\/j.jbi.2026.104998_b21","series-title":"Proceedings of the 34th Neural Information Processing Systems","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","author":"Wu","year":"2021"},{"key":"10.1016\/j.jbi.2026.104998_b22","series-title":"Proceedings of 41st International Conference on Machine Learning","article-title":"A decoder-only foundation model for time-series forecasting","author":"Das","year":"2024"},{"key":"10.1016\/j.jbi.2026.104998_b23","doi-asserted-by":"crossref","unstructured":"Z. Wang, B. Cai, W. Yang, P. Zhang, Y. Huang, J. Tang, Learning High-dimensional Associations for Nonalcoholic Fatty Liver Disease Diagnosis Prediction, in: Proceedings of 2022 IEEE Smartworld, Ubiquitous Intelligence & Computing, Haikou, China, 2022, pp. 194\u2013201.","DOI":"10.1109\/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00052"},{"key":"10.1016\/j.jbi.2026.104998_b24","article-title":"Glu-ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients","volume":"10","author":"Han","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.jbi.2026.104998_b25","first-page":"02","article-title":"Assessing the accuracy of CGM metrics: The role of missing data and imputation strategies","author":"Cichosz","year":"2025","journal-title":"MedRxiv"},{"issue":"2","key":"10.1016\/j.jbi.2026.104998_b26","doi-asserted-by":"crossref","first-page":"384","DOI":"10.3390\/make5020023","article-title":"A diabetes prediction system based on incomplete fused data sources","volume":"5","author":"Yuan","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"10.1016\/j.jbi.2026.104998_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110012","article-title":"Reducing high-risk glucose forecasting errors by evolving interpretable models for type 1 diabetes","volume":"134","author":"Della Cioppa","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.jbi.2026.104998_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.medengphy.2024.104241","article-title":"Platform for precise, personalised glucose forecasting through continuous glucose and physical activity monitoring and deep learning","volume":"132","author":"Kalita","year":"2024","journal-title":"Med. Eng. Phys."},{"key":"10.1016\/j.jbi.2026.104998_b29","doi-asserted-by":"crossref","DOI":"10.3389\/fphys.2023.1225638","article-title":"Heterogeneous temporal representation for diabetic blood glucose prediction","volume":"14","author":"Huang","year":"2023","journal-title":"Front. Physiol."},{"key":"10.1016\/j.jbi.2026.104998_b30","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2023.104498","article-title":"Continuous time recurrent neural networks: Overview and benchmarking at forecasting blood glucose in the intensive care unit","volume":"146","author":"Fitzgerald","year":"2023","journal-title":"J. Biomed. Inform."},{"key":"10.1016\/j.jbi.2026.104998_b31","series-title":"Proceedings of the 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society","first-page":"1680","article-title":"Data-driven strategies for robust forecast of continuous glucose monitoring time-series","author":"Fiorini","year":"2017"},{"issue":"35","key":"10.1016\/j.jbi.2026.104998_b32","article-title":"Chinese diabetes datasets for data-driven machine learning","volume":"10","author":"Zhao","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.jbi.2026.104998_b33","series-title":"Proceedings of the 3rd International Conference for Learning Representations","first-page":"1","article-title":"Adam: A method for stochastic optimization","author":"Kingma","year":"2015"},{"key":"10.1016\/j.jbi.2026.104998_b34","first-page":"6989","article-title":"N-HiTS: Neural hierarchical interpolation for time series forecasting","volume":"vol. 37","author":"Challu","year":"2023"},{"issue":"1","key":"10.1016\/j.jbi.2026.104998_b35","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1007\/s10489-021-02391-6","article-title":"COVID-19 cases prediction in multiple areas via shapelet learning","volume":"52","author":"Wang","year":"2022","journal-title":"Appl. Intell."},{"key":"10.1016\/j.jbi.2026.104998_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2022.116163","article-title":"A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in nordic countries","volume":"269","author":"Ding","year":"2022","journal-title":"Energy Convers. Manage."},{"issue":"2","key":"10.1016\/j.jbi.2026.104998_b37","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"10.1016\/j.jbi.2026.104998_b38","series-title":"Learning phrase representations using RNN encoder-decoder for statistical machine translation","author":"Cho","year":"2014"},{"issue":"8","key":"10.1016\/j.jbi.2026.104998_b39","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"10.1016\/j.jbi.2026.104998_b40","series-title":"SSST\u201914","first-page":"103","article-title":"On the properties of neural machine translation: Encoder-decoder approaches","author":"Cho","year":"2014"},{"key":"10.1016\/j.jbi.2026.104998_b41","series-title":"Proceedings of the 31st International Conference on Advances in Neural Information Processing Systems","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"key":"10.1016\/j.jbi.2026.104998_b42","series-title":"Proceedings of the 35th AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: Beyond efficient transformer for long sequence time-series forecasting","author":"Zhou","year":"2021"},{"key":"10.1016\/j.jbi.2026.104998_b43","series-title":"The Eleventh International Conference on Learning Representations","article-title":"Crossformer: Transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023"},{"key":"10.1016\/j.jbi.2026.104998_b44","unstructured":"Y. Nie, N.H. Nguyen, P. Sinthong, J. Kalagnanam, A Time Series is Worth 64 Words: Long-term Forecasting with Transformers, in: Proceedings of the 11th International Conference on Learning Representations, (ICLR 2023), Kigali, Rwanda, 2023, pp. 1\u201312, http:\/\/dx.doi.org\/10.48550\/arxiv.2211.14730."},{"key":"10.1016\/j.jbi.2026.104998_b45","unstructured":"Y. Liu, T. Hu, H. Zhang, H. Wu, S. Wang, L. Ma, M. Long, iTransformer: Inverted transformers are effective for time series forecasting, in: The Twelfth International Conference on Learning Representations, 2024."},{"key":"10.1016\/j.jbi.2026.104998_b46","series-title":"Timer: Generative pre-trained transformers are large time series models","author":"Liu","year":"2024"},{"key":"10.1016\/j.jbi.2026.104998_b47","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109806","article-title":"Dual-grained directional representation for infectious disease case prediction","volume":"256","author":"Zhang","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"10.1016\/j.jbi.2026.104998_b48","series-title":"Time Series Analysis: Forecasting and Control","author":"Box","year":"2015"},{"issue":"5","key":"10.1016\/j.jbi.2026.104998_b49","doi-asserted-by":"crossref","first-page":"3123","DOI":"10.1109\/JBHI.2023.3348334","article-title":"Exploring nutritional influence on blood glucose forecasting for type 1 diabetes using explainable AI","volume":"28","author":"Annuzzi","year":"2024","journal-title":"IEEE J. Biomed. Health Inform."}],"container-title":["Journal of Biomedical Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046426000225?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1532046426000225?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,20]],"date-time":"2026-03-20T22:41:40Z","timestamp":1774046500000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1532046426000225"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":49,"alternative-id":["S1532046426000225"],"URL":"https:\/\/doi.org\/10.1016\/j.jbi.2026.104998","relation":{},"ISSN":["1532-0464"],"issn-type":[{"value":"1532-0464","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"AFTS: A patient-agnostic encoder\u2013decoder architecture with directional attention for blood glucose forecasting","name":"articletitle","label":"Article Title"},{"value":"Journal of Biomedical Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jbi.2026.104998","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Published by Elsevier Inc.","name":"copyright","label":"Copyright"}],"article-number":"104998"}}