{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,9]],"date-time":"2026-04-09T14:43:55Z","timestamp":1775745835739,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":20,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,10,18]],"date-time":"2024-10-18T00:00:00Z","timestamp":1729209600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,10,18]]},"DOI":"10.1145\/3704198.3704211","type":"proceedings-article","created":{"date-parts":[[2025,2,17]],"date-time":"2025-02-17T06:29:01Z","timestamp":1739773741000},"page":"104-112","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["An LSTM-Based Model for Non-invasive Blood Glucose Prediction Utilizing BVP Signals"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-5474-0211","authenticated-orcid":false,"given":"Yuhuan","family":"Cui","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Zhejiang Normal University, Jinhua, Zhejiang, China,"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-9778-9938","authenticated-orcid":false,"given":"Shitu","family":"Ma","sequence":"additional","affiliation":[{"name":"Queen Mary School Hainan, Beijing University of Posts and Telecommunications, Beijing, Beijing, China,"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1006-3211","authenticated-orcid":false,"given":"Xiaoyu","family":"Wu","sequence":"additional","affiliation":[{"name":"School Of Life Sciences, Sun Yat-Sen University, Guangzhou, Guangdong, China,"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-9185-3313","authenticated-orcid":false,"given":"Boyu","family":"Wang","sequence":"additional","affiliation":[{"name":"International school, Beijing university of Post and Telecommunications, Beijing, Beijing, China,"}]}],"member":"320","published-online":{"date-parts":[[2025,2,16]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"crossref","unstructured":"T\u00f6nnies T. Quantifying the underestimation of projected global diabetes prevalence by the International Diabetes Federation (IDF) Diabetes Atlas[J]. BMJ open diabetes research & care 2021 9(1).","DOI":"10.1136\/bmjdrc-2021-002122"},{"key":"e_1_3_3_1_2_2","first-page":"1148","volume":"2022","author":"Gu Nan G.","unstructured":"Gu Nan, Xiaohui G. Difficulties and prospects of standardized management of type 1 diabetes[J]. Natl Med J China,2022,102(16):1148-1152","journal-title":"Natl Med J China"},{"key":"e_1_3_3_1_3_2","first-page":"550","volume":"2021","author":"Zhidong Liu","unstructured":"Zhidong Liu, Shigao Zhou, Kepei Zhang, et al. Analysis and Research Progress of Type 2 Diabetes Combined with Obesity in Traditional Chinese Medicine[J]. Traditional Chinese Medicine,2021,10(4):550-557.","journal-title":"Traditional Chinese Medicine"},{"key":"e_1_3_3_1_4_2","first-page":"2254","volume":"2021","author":"Wang Yudong","unstructured":"Wang Yudong, Fu Guifen, Han Jiaxia, etc Application status of implantable continuous glucose monitoring system in patients with diabetes [J]. Guangxi Medical Journal,2021,43(18):2254-2257.","journal-title":"Guangxi Medical Journal"},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"crossref","unstructured":"Yan J Cai X Zhu G et al. A non-invasive blood pressure prediction method based on pulse wave feature fusion[J]. Biomedical signal processing and control 2022(Apr.):74.","DOI":"10.1016\/j.bspc.2022.103523"},{"key":"e_1_3_3_1_6_2","doi-asserted-by":"crossref","unstructured":"Susana E Ramli K Murfi H et al. Non-Invasive Classification of Blood Glucose Level for Early Detection Diabetes Based on Photoplethysmography Signal[J]. Information 2022 13(2) 59.","DOI":"10.3390\/info13020059"},{"key":"e_1_3_3_1_7_2","first-page":"652","volume":"2020","author":"Zhou X","unstructured":"Zhou X, Ling B W K, Tian Z, et al. Joint empirical mode decomposition, exponential function estimation andL 1 norm approach for estimating mean value of photoplethysmogram and blood glucose level[J]. IET Signal Processing,2020,14(9):652-665.","journal-title":"IET Signal Processing"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Zanelli S Ammi M Hallab M et al. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review[J]. Sensors (Basel) 2022 22(13).","DOI":"10.3390\/s22134890"},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.1109\/ISCAS.2019.8702747"},{"key":"e_1_3_3_1_10_2","doi-asserted-by":"crossref","unstructured":"Shi B Dhaliwal S S Soo M et al. Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation[J]. JMIR AI 2023 2:e48340.","DOI":"10.2196\/48340"},{"key":"e_1_3_3_1_11_2","unstructured":"Cho P K J B. BIG IDEAs Lab Glycemic Variability and Wearable Device Data (version 1.1.2). [J]. PhysioNet. 2023."},{"key":"e_1_3_3_1_12_2","first-page":"720","volume":"2014","author":"Dunn T C","unstructured":"Dunn T C, Hayter G A, Doniger K J, et al. Development of the Likelihood of Low Glucose (LLG) Algorithm for Evaluating Risk of Hypoglycemia: A New Approach for Using Continuous Glucose Data to Guide Therapeutic Decision Making[J]. Journal of Diabetes Science & Technology,2014,8(4):720-730.","journal-title":"Journal of Diabetes Science & Technology"},{"key":"e_1_3_3_1_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/EMBC.2015.7320243"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"crossref","unstructured":"Geng Z Tang F Ding Y et al. Noninvasive Continuous Glucose Monitoring Using a Multisensor-Based Glucometer and Time Series Analysis[J]. Sci Rep. 2017 7(1):12650.","DOI":"10.1038\/s41598-017-13018-7"},{"key":"e_1_3_3_1_15_2","volume-title":"A Deep Learning Algorithm for Personalized Blood Glucose Prediction.[C]","author":"Zhu T","year":"2018","unstructured":"Zhu T, Li K, Herrero P, et al. A Deep Learning Algorithm for Personalized Blood Glucose Prediction.[C]. 2018."},{"key":"e_1_3_3_1_16_2","volume-title":"Strategies of Multi-Step-ahead Forecasting for Blood Glucose Level using LSTM Neural Networks: A Comparative Study[J]","author":"Idrissi T E","year":"2020","unstructured":"Idrissi T E, Idri A, Kadi I, et al. Strategies of Multi-Step-ahead Forecasting for Blood Glucose Level using LSTM Neural Networks: A Comparative Study[J]. 2020."},{"key":"e_1_3_3_1_17_2","doi-asserted-by":"crossref","unstructured":"Nasser A R Hasan A M Humaidi A J et al. IoT and Cloud Computing in Health-Care: A New Wearable Device and Cloud-Based Deep Learning Algorithm for Monitoring of Diabetes[J]. Electronics 2021 10(21) 2719.","DOI":"10.3390\/electronics10212719"},{"key":"e_1_3_3_1_18_2","first-page":"949","volume":"2021","author":"China Group On Insulin Secretion, Yanbing L, Abdollahiyan S. Consensus statement on reversal of type 2 diabetes using short\u2011term intensive insulin therapy[J].","unstructured":"China Group On Insulin Secretion, Yanbing L, Abdollahiyan S. Consensus statement on reversal of type 2 diabetes using short\u2011term intensive insulin therapy[J]. ChinJDiabetesMellitus,2021,13(10):949-959.","journal-title":"ChinJDiabetesMellitus"},{"key":"e_1_3_3_1_19_2","volume-title":"Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes\u20142023}[J]. Diabetes Care,2022,46(Supplement_1):S68-S96","author":"Elsayed N A","unstructured":"Elsayed N A, Aleppo G, Aroda V R, et al. {5. Facilitating Positive Health Behaviors and Well-being to Improve Health Outcomes: Standards of Care in Diabetes\u20142023}[J]. Diabetes Care,2022,46(Supplement_1):S68-S96."},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Naicker K Johnson J A Skogen J C et al. Type 2 Diabetes and Comorbid Symptoms of Depression and Anxiety: Longitudinal Associations With Mortality Risk[J]. Diabetes Care 2017 40(3):dc162018.","DOI":"10.2337\/dc16-2018"}],"event":{"name":"ICBBS 2024: 2024 13th International Conference on Bioinformatics and Biomedical Science","location":"Hong Kong Guangdong Hong Kong","acronym":"ICBBS 2024"},"container-title":["Proceedings of the 2024 13th International Conference on Bioinformatics and Biomedical Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704198.3704211","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3704198.3704211","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:18:07Z","timestamp":1750295887000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3704198.3704211"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,10,18]]},"references-count":20,"alternative-id":["10.1145\/3704198.3704211","10.1145\/3704198"],"URL":"https:\/\/doi.org\/10.1145\/3704198.3704211","relation":{},"subject":[],"published":{"date-parts":[[2024,10,18]]},"assertion":[{"value":"2025-02-16","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}