{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,13]],"date-time":"2026-05-13T00:56:32Z","timestamp":1778633792308,"version":"3.51.4"},"reference-count":64,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2022,11,3]],"date-time":"2022-11-03T00:00:00Z","timestamp":1667433600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["62072488"],"award-info":[{"award-number":["62072488"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Nature Science Foundation of China","doi-asserted-by":"publisher","award":["4202064"],"award-info":[{"award-number":["4202064"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["62072488"],"award-info":[{"award-number":["62072488"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004826","name":"Beijing Natural Science Foundation","doi-asserted-by":"publisher","award":["4202064"],"award-info":[{"award-number":["4202064"]}],"id":[{"id":"10.13039\/501100004826","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Diabetes is an increasingly common disease that poses an immense challenge to public health. Hyperglycemia is also a common complication in clinical patients in the intensive care unit, increasing the rate of infection and mortality. The accurate and real-time prediction of blood glucose concentrations after each short-acting insulin injection has great clinical significance and is the basis of all intelligent blood glucose control systems. Most previous prediction methods require long-term continuous blood glucose records from specific patients to train the prediction models, resulting in these methods not being used in clinical practice. In this study, we construct 13 deep neural networks with different architectures to atomically predict blood glucose concentrations after arbitrary independent insulin injections without requiring continuous historical records of any patient. Using our proposed models, the best root mean square error of the prediction results reaches 15.82 mg\/dL, and 99.5% of the predictions are clinically acceptable, which is more accurate than previously proposed blood glucose prediction methods. Through the re-validation of the models, we demonstrate the clinical practicability and universal accuracy of our proposed prediction method.<\/jats:p>","DOI":"10.3390\/s22218454","type":"journal-article","created":{"date-parts":[[2022,11,4]],"date-time":"2022-11-04T04:00:51Z","timestamp":1667534451000},"page":"8454","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting Blood Glucose Concentration after Short-Acting Insulin Injection Using Discontinuous Injection Records"],"prefix":"10.3390","volume":"22","author":[{"given":"Baoyu","family":"Tang","sequence":"first","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuyu","family":"Yuan","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jincui","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lirong","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shasha","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinsheng","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, China"},{"name":"Key Laboratory of Trustworthy Distributed Computing and Service, Ministry of Education, Beijing 100876, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,3]]},"reference":[{"key":"ref_1","unstructured":"(2022, June 21). IDF Diabetes Atlas. Available online: https:\/\/www.diabetesatlas.org."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10718","DOI":"10.1038\/s41598-020-67864-z","article-title":"Real-world characterization of blood glucose control and insulin use in the intensive care unit","volume":"10","author":"Baker","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1186\/s13054-016-1471-6","article-title":"Stress hyperglycaemia in critically ill patients and the subsequent risk of diabetes: A systematic review and meta-analysis","volume":"20","author":"Abdelhamid","year":"2016","journal-title":"Crit. Care"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1186\/cc12514","article-title":"Stress hyperglycemia: An essential survival response!","volume":"17","author":"Marik","year":"2013","journal-title":"Crit. Care"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"978","DOI":"10.1210\/jcem.87.3.8341","article-title":"Hyperglycemia: An Independent Marker of In-Hospital Mortality in Patients with Undiagnosed Diabetes","volume":"87","author":"Umpierrez","year":"2002","journal-title":"J. Clin. Endocrinol. Metab."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"2772","DOI":"10.1097\/01.CCM.0000189741.44071.25","article-title":"Impact of admission hyperglycemia on hospital mortality in various intensive care unit populations","volume":"33","author":"Whitcomb","year":"2005","journal-title":"Crit. Care Med."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1613","DOI":"10.1001\/archinte.166.15.1613","article-title":"Admission blood glucose level and mortality among hospitalized nondiabetic patients with heart failure","volume":"166","author":"Barsheshet","year":"2006","journal-title":"Arch. Intern. Med."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1388","DOI":"10.1097\/CCM.0b013e3181d8a38b","article-title":"Hyperglycemia-related mortality in critically ill patients varies with admission diagnosis","volume":"38","author":"Preiser","year":"2010","journal-title":"Crit. Care Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"71","DOI":"10.5935\/0103-507X.20140011","article-title":"Assessment and treatment of hyperglycemia in critically ill patients","volume":"26","author":"Viana","year":"2014","journal-title":"Rev. Bras. Ter. Intensiv."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e1525","DOI":"10.1097\/MD.0000000000001525","article-title":"Usefulness of glycemic gap to predict ICU mortality in critically ill patients with diabetes","volume":"94","author":"Liao","year":"2015","journal-title":"Medicine"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1056\/NEJMoa011300","article-title":"Intensive Insulin Therapy in Critically Ill Patients","volume":"345","author":"Wouters","year":"2001","journal-title":"N. Engl. J. Med."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"2626","DOI":"10.1161\/01.CIR.99.20.2626","article-title":"Glycometabolic State at Admission: Important Risk Marker of Mortality in Conventionally Treated Patients With Diabetes Mellitus and Acute Myocardial Infarction","volume":"99","author":"Malmberg","year":"1999","journal-title":"Circulation"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1067\/mtc.2003.181","article-title":"Continuous insulin infusion reduces mortality in patients with diabetes undergoing coronary artery bypass grafting","volume":"125","author":"Furnary","year":"2003","journal-title":"J. Thorac. Cardiovasc. Surg."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"449","DOI":"10.1056\/NEJMoa052521","article-title":"Intensive Insulin Therapy in the Medical ICU","volume":"354","author":"Wilmer","year":"2006","journal-title":"N. Engl. J. Med."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1048","DOI":"10.1016\/j.jamcollsurg.2006.12.047","article-title":"Intensive Insulin Protocol Improves Glucose Control and Is Associated with a Reduction in Intensive Care Unit Mortality","volume":"204","author":"Reed","year":"2007","journal-title":"J. Am. Coll. Surg."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1016\/S0140-6736(09)60044-1","article-title":"Intensive insulin therapy for patients in paediatric intensive care: A prospective, randomised controlled study","volume":"373","author":"Vlasselaers","year":"2009","journal-title":"Lancet"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1016\/j.jfda.2014.12.001","article-title":"Recent developments in blood glucose sensors","volume":"23","author":"Wang","year":"2015","journal-title":"J. Food Drug Anal."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1037\/h0042519","article-title":"The perceptron: A probabilistic model for information storage and organization in the brain","volume":"65","author":"Rosenblatt","year":"1958","journal-title":"Psychol. Rev."},{"key":"ref_19","unstructured":"Werbos, P. (1974). Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. [Ph.D. Thesis, Harvard University]."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"533","DOI":"10.1038\/323533a0","article-title":"Learning representations by back-propagating errors","volume":"323","author":"Rumelhart","year":"1986","journal-title":"Nature"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Fukushima, K., and Miyake, S. (1982, January 15\u201319). Neocognitron: A Self-Organizing Neural Network Model for a Mechanism of Visual Pattern Recognition. Proceedings of the Competition and Cooperation in Neural Nets, Kyoto, Japan.","DOI":"10.1007\/978-3-642-46466-9_18"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1109\/29.21701","article-title":"Phoneme recognition using time-delay neural networks","volume":"37","author":"Waibel","year":"1989","journal-title":"IEEE Trans. Acoust. Speech Signal Process."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A Fast Learning Algorithm for Deep Belief Nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"30","DOI":"10.1109\/TASL.2011.2134090","article-title":"Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition","volume":"20","author":"Dahl","year":"2012","journal-title":"IEEE Trans. Audio Speech Lang. Process."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"ImageNet Classification with Deep Convolutional Neural Networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Schroff, F., Kalenichenko, D., and Philbin, J. (2015, January 7\u201312). FaceNet: A unified embedding for face recognition and clustering. Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298682"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"484","DOI":"10.1038\/nature16961","article-title":"Mastering the game of Go with deep neural networks and tree search","volume":"529","author":"Silver","year":"2016","journal-title":"Nature"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1099","DOI":"10.1001\/jama.2018.11103","article-title":"On the Prospects for a (Deep) Learning Health Care System","volume":"320","author":"Naylor","year":"2018","journal-title":"JAMA"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1001\/jama.2018.11029","article-title":"Clinical Implications and Challenges of Artificial Intelligence and Deep Learning","volume":"320","author":"Stead","year":"2018","journal-title":"JAMA"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1101","DOI":"10.1001\/jama.2018.11100","article-title":"Deep Learning\u2014A Technology With the Potential to Transform Health Care","volume":"320","author":"Hinton","year":"2018","journal-title":"JAMA"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2199","DOI":"10.1001\/jama.2017.14585","article-title":"Diagnostic Assessment of Deep Learning Algorithms for Detection of Lymph Node Metastases in Women With Breast Cancer","volume":"318","author":"Veta","year":"2017","journal-title":"JAMA"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1342","DOI":"10.1038\/s41591-018-0107-6","article-title":"Clinically applicable deep learning for diagnosis and referral in retinal disease","volume":"24","author":"Ledsam","year":"2018","journal-title":"Nat. Med."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1760","DOI":"10.1038\/s41467-020-15432-4","article-title":"Automatic diagnosis of the 12-lead ECG using a deep neural network","volume":"11","author":"Ribeiro","year":"2020","journal-title":"Nat. Commun."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"5117","DOI":"10.1038\/s41467-021-25351-7","article-title":"Deep neural network-estimated electrocardiographic age as a mortality predictor","volume":"12","author":"Lima","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"750","DOI":"10.1038\/s41467-018-03113-2","article-title":"Enhancing Hi-C data resolution with deep convolutional neural network HiCPlus","volume":"9","author":"Zhang","year":"2018","journal-title":"Nat. Commun."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"128","DOI":"10.1016\/j.media.2018.11.010","article-title":"A deep learning framework for unsupervised affine and deformable image registration","volume":"52","author":"Berendsen","year":"2019","journal-title":"Med. Image Anal."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1186\/s13014-019-1392-z","article-title":"Comparative clinical evaluation of atlas and deep-learning-based auto-segmentation of organ structures in liver cancer","volume":"14","author":"Ahn","year":"2019","journal-title":"Radiat. Oncol."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"715","DOI":"10.1038\/s41467-021-20966-2","article-title":"Deep convolutional neural networks to predict cardiovascular risk from computed tomography","volume":"12","author":"Zeleznik","year":"2021","journal-title":"Nat. Commun."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Alghamdi, M., Al-Mallah, M., Keteyian, S., Brawner, C., Ehrman, J., and Sakr, S. (2017). Predicting diabetes mellitus using SMOTE and ensemble machine learning approach: The Henry Ford ExercIse Testing (FIT) project. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0179805"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1016\/j.neucom.2018.11.112","article-title":"Automated hepatobiliary toxicity prediction after liver stereotactic body radiation therapy with deep learning-based portal vein segmentation","volume":"392","author":"Ibragimov","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"8246","DOI":"10.1088\/1361-6560\/aa8d09","article-title":"Deep convolutional neural network with transfer learning for rectum toxicity prediction in cervical cancer radiotherapy: A feasibility study","volume":"62","author":"Zhen","year":"2017","journal-title":"Phys. Med. Biol."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1177\/1932296818759558","article-title":"A Neural-Network-Based Approach to Personalize Insulin Bolus Calculation Using Continuous Glucose Monitoring","volume":"12","author":"Cappon","year":"2018","journal-title":"J. Diabetes Sci. Technol."},{"key":"ref_45","doi-asserted-by":"crossref","unstructured":"Padmapritha, T. (2019, January 18\u201320). Prediction of Blood Glucose Level by using an LSTM based Recurrent Neural networks. Proceedings of the 2019 IEEE International Conference on Clean Energy and Energy Efficient Electronics Circuit for Sustainable Development (INCCES), Krishnankoil, India.","DOI":"10.1109\/INCCES47820.2019.9167734"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Song, W., Cai, W., Li, J., Jiang, F., and He, S. (2019, January 2\u20134). Predicting Blood Glucose Levels with EMD and LSTM Based CGM Data. Proceedings of the 2019 6th International Conference on Systems and Informatics (ICSAI), Shanghai, China.","DOI":"10.1109\/ICSAI48974.2019.9010318"},{"key":"ref_47","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":"ref_48","doi-asserted-by":"crossref","first-page":"603","DOI":"10.1109\/JBHI.2019.2908488","article-title":"Convolutional Recurrent Neural Networks for Glucose Prediction","volume":"24","author":"Li","year":"2020","journal-title":"IEEE J. Biomed. Health Inform."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Mirshekarian, S., Bunescu, R., Marling, C., and Schwartz, F. (2017, January 11\u201315). Using LSTMs to learn physiological models of blood glucose behavior. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Jeju, Korea.","DOI":"10.1109\/EMBC.2017.8037460"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Dudukcu, H.V., Taskiran, M., and Yildirim, T. (2021, January 25\u201327). Consolidated or individual training: Which one is better for blood glucose prediction?. Proceedings of the 2021 International Conference on INnovations in Intelligent SysTems and Applications (INISTA), Kocaeli, Turkey.","DOI":"10.1109\/INISTA52262.2021.9548612"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"14","DOI":"10.3389\/fams.2017.00014","article-title":"A Deep Learning Approach to Diabetic Blood Glucose Prediction","volume":"3","author":"Mhaskar","year":"2017","journal-title":"Front. Appl. Math. Stat."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1089\/dia.2010.0104","article-title":"Neural Network-Based Real-Time Prediction of Glucose in Patients with Insulin-Dependent Diabetes","volume":"13","author":"Pappada","year":"2011","journal-title":"Diabetes Technol. Ther."},{"key":"ref_53","unstructured":"Johnson, A., Bulgarelli, L., Pollard, T., Horng, S., Celi, L.A., and Mark., R. (2021). MIMIC-IV (version 1.0). PhysioNet."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.diabres.2015.10.015","article-title":"Prevalence and correlates of diagnosed and undiagnosed type 2 diabetes mellitus and pre-diabetes in older adults: Findings from the Irish Longitudinal Study on Ageing (TILDA)","volume":"110","author":"Leahy","year":"2015","journal-title":"Diabetes Res. Clin. Pract."},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Alhyas, L., McKay, A., and Majeed, A. (2012). Prevalence of Type 2 Diabetes in the States of The Co-Operation Council for the Arab States of the Gulf: A Systematic Review. PLoS ONE, 7.","DOI":"10.1371\/journal.pone.0040948"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.2337\/diacare.27.5.1047","article-title":"Global Prevalence of Diabetes: Estimates for the year 2000 and projections for 2030","volume":"27","author":"Wild","year":"2004","journal-title":"Diabetes Care"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"965","DOI":"10.1093\/oxfordjournals.aje.a116408","article-title":"Race\/Ethnicity and Other Risk Factors for Gestational Diabetes","volume":"135","author":"Berkowitz","year":"1992","journal-title":"Am. J. Epidemiol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2389","DOI":"10.1001\/jama.2019.19365","article-title":"Prevalence of Diabetes by Race and Ethnicity in the United States, 2011\u20132016","volume":"322","author":"Cheng","year":"2019","journal-title":"JAMA"},{"key":"ref_59","first-page":"71","article-title":"Promoters of progression of diabetic nephropathy: The relative roles of blood glucose and blood pressure control","volume":"12","author":"Alaveras","year":"1997","journal-title":"Nephrol. Dial. Transplant. Off. Publ. Eur. Dial. Transpl. Assoc.-Eur. Ren. Assoc."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"437","DOI":"10.1007\/BF00261697","article-title":"Glycosylated haemoglobin in renal failure","volume":"18","author":"Miedema","year":"1980","journal-title":"Diabetologia"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"776","DOI":"10.1089\/dia.2005.7.776","article-title":"The Original Clarke Error Grid Analysis (EGA)","volume":"7","author":"Clarke","year":"2005","journal-title":"Diabetes Technol. Ther."},{"key":"ref_62","first-page":"2825","article-title":"Scikit-Learn: Machine Learning in Python","volume":"12","author":"Pedregosa","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_63","first-page":"1089","article-title":"No Unbiased Estimator of the Variance of K-Fold Cross-Validation","volume":"5","author":"Bengio","year":"2004","journal-title":"J. Mach. Learn. Res."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"87","DOI":"10.2307\/2340521","article-title":"On the Interpretation of \u03c72 from Contingency Tables, and the Calculation of P","volume":"85","author":"Fisher","year":"1922","journal-title":"J. R. Stat. Soc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8454\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:09:51Z","timestamp":1760144991000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/21\/8454"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,11,3]]},"references-count":64,"journal-issue":{"issue":"21","published-online":{"date-parts":[[2022,11]]}},"alternative-id":["s22218454"],"URL":"https:\/\/doi.org\/10.3390\/s22218454","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,11,3]]}}}