{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T20:14:01Z","timestamp":1784751241296,"version":"3.55.0"},"reference-count":40,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62002187"],"award-info":[{"award-number":["62002187"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972237"],"award-info":[{"award-number":["61972237"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61876102"],"award-info":[{"award-number":["61876102"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62276156"],"award-info":[{"award-number":["62276156"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2024LZH005"],"award-info":[{"award-number":["ZR2024LZH005"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"publisher","award":["2024HWYQ-055"],"award-info":[{"award-number":["2024HWYQ-055"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007129","name":"Shandong Province Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2024QF306"],"award-info":[{"award-number":["ZR2024QF306"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,11]]},"DOI":"10.1016\/j.bspc.2026.111066","type":"journal-article","created":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T06:53:26Z","timestamp":1784530406000},"page":"111066","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PA","title":["Frequency-Enhanced Time-Series forecasting for refined disease progression prediction in critically ill patients"],"prefix":"10.1016","volume":"127","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-7157-7092","authenticated-orcid":false,"given":"Zhengxu","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3238-9888","authenticated-orcid":false,"given":"Chunyu","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Zhuang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1724-9625","authenticated-orcid":false,"given":"Zengjie","family":"Dong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.111066_b0005","doi-asserted-by":"crossref","first-page":"2012","DOI":"10.1056\/NEJMoa2004500","article-title":"Covid-19 in critically ill patients in the Seattle region\u2014case series","volume":"382","author":"Bhatraju","year":"2020","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.bspc.2026.111066_b0010","doi-asserted-by":"crossref","first-page":"311","DOI":"10.1111\/j.1365-2834.2011.01246.x","article-title":"Monitoring vital signs using early warning scoring systems: a review of the literature","volume":"19","author":"Kyriacos","year":"2011","journal-title":"J. Nurs. Manag."},{"key":"10.1016\/j.bspc.2026.111066_b0015","unstructured":"Y. Wang, H. Wu, J. Dong, Y. Liu, M. Long, J. Wang, Deep time series models: A comprehensive survey and benchmark, arXiv:2407.13278, 2024. doi: 10.48550\/arXiv.2407.13278."},{"key":"10.1016\/j.bspc.2026.111066_b0020","first-page":"1237","article-title":"L.-w.H. Lehman, forecasting Treatment Outcomes over Time using Alternating Deep Sequential Models","volume":"71","author":"Wu","year":"2023","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.111066_b0025","doi-asserted-by":"crossref","first-page":"58","DOI":"10.3934\/publichealth.2024004","article-title":"Machine learning and deep learning-based approach in smart healthcare: recent advances, applications, challenges and opportunities","volume":"11","author":"Rahman","year":"2024","journal-title":"AIMS Public Health"},{"key":"10.1016\/j.bspc.2026.111066_b0030","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.neucom.2021.02.046","article-title":"A review of irregular time series data handling with gated recurrent neural networks","volume":"441","author":"Weerakody","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.111066_b0035","doi-asserted-by":"crossref","first-page":"517","DOI":"10.3390\/info15090517","article-title":"Recurrent neural networks: a comprehensive review of architectures, variants, and applications","volume":"15","author":"Mienye","year":"2024","journal-title":"Information"},{"key":"10.1016\/j.bspc.2026.111066_b0040","doi-asserted-by":"crossref","first-page":"76656","DOI":"10.52202\/075280-3349","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","volume":"36","author":"Yi","year":"2023","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.111066_b0045","series-title":"In: Proceedings of the 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.bspc.2026.111066_b0050","unstructured":"T. Zhou, Z. Ma, Q. Wen, X. Wang, L. Sun, R. Jin, FEDformer: Frequency Enhanced Decomposed Transformer for Long-term Series Forecasting, in: Proceedings of the 39th International Conference on Machine Learning, Proc. Mach. Learn. Res. 162 (2022) 27268\u201327286."},{"key":"10.1016\/j.bspc.2026.111066_b0055","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0251248","article-title":"MGP-AttTCN: an interpretable machine learning model for the prediction of sepsis","volume":"16","author":"Rosnati","year":"2021","journal-title":"PLoS One"},{"key":"10.1016\/j.bspc.2026.111066_b0060","series-title":"In: 2025 International Conference on Multi-Agent Systems for Collaborative Intelligence (ICMSCI)","first-page":"1800","article-title":"Patient monitoring based on ICU records using hybrid TCN-LSTM model","author":"Sk","year":"2025"},{"key":"10.1016\/j.bspc.2026.111066_b0065","series-title":"In: the Twelfth International Conference on Learning Representations","article-title":"Long, iTransformer: Inverted Transformers are Effective for Time Series forecasting","author":"Liu","year":"2024"},{"key":"10.1016\/j.bspc.2026.111066_b0070","unstructured":"S. Bai, J.Z. Kolter, V. Koltun, An empirical evaluation of generic convolutional and recurrent networks for sequence modeling, arXiv preprint arXiv:1803.01271, (2018). doi: 10.48550\/arXiv.1803.01271."},{"key":"10.1016\/j.bspc.2026.111066_b0075","doi-asserted-by":"crossref","DOI":"10.1016\/j.physd.2019.132306","article-title":"Fundamentals of recurrent neural network (RNN) and long short-term memory (LSTM) network","volume":"404","author":"Sherstinsky","year":"2020","journal-title":"Phys. D"},{"key":"10.1016\/j.bspc.2026.111066_b0080","series-title":"Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence","first-page":"49","article-title":"A comparison between ARIMA, LSTM, and GRU for time series forecasting, in","author":"Yamak","year":"2019"},{"key":"10.1016\/j.bspc.2026.111066_b0085","doi-asserted-by":"crossref","DOI":"10.1016\/j.rinp.2021.104462","article-title":"Time series prediction of COVID-19 transmission in America using LSTM and XGBoost algorithms","volume":"27","author":"Luo","year":"2021","journal-title":"Results Phys."},{"key":"10.1016\/j.bspc.2026.111066_b0090","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.neucom.2018.09.082","article-title":"Time series forecasting of petroleum production using deep LSTM recurrent networks","volume":"323","author":"Sagheer","year":"2019","journal-title":"Neurocomputing"},{"key":"10.1016\/j.bspc.2026.111066_b0095","article-title":"Personalized Blood Glucose forecasting from Limited CGM Data using incrementally Retrained LSTM","author":"Shen","year":"2024","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.111066_b0100","first-page":"1","article-title":"Deep learning for time series forecasting: a survey","author":"Kong","year":"2025","journal-title":"Int. J. Mach. Learn. Cybern."},{"key":"10.1016\/j.bspc.2026.111066_b0105","first-page":"193","article-title":"Personalized blood glucose prediction for type 1 diabetes using evidential deep learning and meta-learning","volume":"70","author":"Zhu","year":"2022","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.111066_b0110","doi-asserted-by":"crossref","unstructured":"M. Ali, C. Lisle, P.W. Moore, T. Barkouki, B.J. Kirkwood, L.J. Brattain, Fine-Tuning Foundation Models with Federated Learning for Privacy Preserving Medical Time Series Forecasting, arXiv preprint arXiv:2502.09744, (2025). doi: 10.48550\/arXiv.2502.09744.","DOI":"10.1109\/EMBC58623.2025.11254049"},{"key":"10.1016\/j.bspc.2026.111066_b0115","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1007\/s13755-024-00313-7","article-title":"A new multivariate blood glucose prediction method with hybrid feature clustering and online transfer learning","volume":"12","author":"You","year":"2024","journal-title":"Health Inf. Sci. Syst."},{"key":"10.1016\/j.bspc.2026.111066_b0120","doi-asserted-by":"crossref","first-page":"1590","DOI":"10.1177\/19322968221092785","article-title":"Long-term prediction of blood glucose levels in type 1 diabetes using a cnn-lstm-based deep neural network","volume":"17","author":"Jaloli","year":"2023","journal-title":"J. Diabetes Sci. Technol."},{"key":"10.1016\/j.bspc.2026.111066_b0125","doi-asserted-by":"crossref","first-page":"8016","DOI":"10.3390\/s22208016","article-title":"A systematic review of time series classification techniques used in biomedical applications","volume":"22","author":"Wang","year":"2022","journal-title":"Sensors"},{"key":"10.1016\/j.bspc.2026.111066_b0130","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.3390\/app9071345","article-title":"Wavelet transform application for\/in non-stationary time-series analysis: a review","volume":"9","author":"Rhif","year":"2019","journal-title":"Appl. Sci."},{"key":"10.1016\/j.bspc.2026.111066_b0135","first-page":"541","article-title":"Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform","volume":"62","author":"Samiee","year":"2014","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.111066_b0140","article-title":"Effectiveness of Multi Input Data and a Novel CNN Model for Epilepsy Classification","volume":"42","author":"Ghezala","year":"2025","journal-title":"Trait. Signal."},{"key":"10.1016\/j.bspc.2026.111066_b0145","article-title":"A novel nicu sleep state stratification: Multiperspective features, adaptive feature selection and ensemble model","author":"Irfan","year":"2025","journal-title":"I.E.E.E. Trans. Biomed. Eng."},{"key":"10.1016\/j.bspc.2026.111066_b0150","series-title":"In: the Eleventh International Conference on Learning Representations","article-title":"TimesNet Temporal 2D-Variation Modeling for General Time Series Analysis","author":"Wu","year":"2023"},{"key":"10.1016\/j.bspc.2026.111066_b0155","doi-asserted-by":"crossref","first-page":"801","DOI":"10.1001\/jama.2016.0287","article-title":"The third international consensus definitions for sepsis and septic shock (Sepsis-3)","volume":"315","author":"Singer","year":"2016","journal-title":"JAMA"},{"key":"10.1016\/j.bspc.2026.111066_b0160","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41597-022-01899-x","article-title":"MIMIC-IV, a freely accessible electronic health record dataset","volume":"10","author":"Johnson","year":"2023","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.111066_b0165","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/sdata.2018.178","article-title":"The eICU Collaborative Research Database, a freely available multi-center database for critical care research","volume":"5","author":"Pollard","year":"2018","journal-title":"Sci. Data"},{"key":"10.1016\/j.bspc.2026.111066_b0170","first-page":"1597","article-title":"Gate-variants of gated recurrent unit (GRU) neural networks, in, IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)","volume":"2017","author":"Dey","year":"2017","journal-title":"IEEE, Boston, MA, USA"},{"key":"10.1016\/j.bspc.2026.111066_b0175","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: LSTM cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"10.1016\/j.bspc.2026.111066_b0180","article-title":"Attention is all you need","volume":"30","author":"Vaswani","year":"2017","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.111066_b0185","doi-asserted-by":"crossref","first-page":"469","DOI":"10.52202\/079017-0015","article-title":"TimeXer: Empowering Transformers for Time Series forecasting with Exogenous Variables","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.bspc.2026.111066_b0190","doi-asserted-by":"crossref","DOI":"10.1103\/PhysRevLett.89.068102","article-title":"Multiscale entropy analysis of complex physiologic time series","volume":"89","author":"Costa","year":"2002","journal-title":"Phys. Rev. Lett."},{"key":"10.1016\/j.bspc.2026.111066_b0195","series-title":"National Early Warning Score (NEWS) 2: Standardising the assessment of acute-illness severity in the NHS","author":"Royal College of Physicians","year":"2017"},{"key":"10.1016\/j.bspc.2026.111066_b0200","doi-asserted-by":"crossref","first-page":"521","DOI":"10.1093\/qjmed\/94.10.521","article-title":"Validation of a modified Early Warning score in medical admissions","volume":"94","author":"Subbe","year":"2001","journal-title":"QJM"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426016204?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426016204?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T19:30:06Z","timestamp":1784748606000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426016204"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":40,"alternative-id":["S1746809426016204"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.111066","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Frequency-Enhanced Time-Series forecasting for refined disease progression prediction in critically ill patients","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.111066","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111066"}}