Blood glucose prediction model for type 1 diabetes based on artificial neural network with time-domain features
Tài liệu tham khảo
Association AD, 2018, Introduction: Standards of medical care in Diabetesd 2018, Diabetes Care, 41, S1, 10.2337/dc18-Sint01
Hankey, 2013, Rates and predictors of risk of stroke and its subtypes in diabetes: a prospective observational study, J Neurol Neurosurg Psychiatry, 84, 281, 10.1136/jnnp-2012-303365
Tun, 2017, Diabetes mellitus and stroke: a clinical update, World J Diabetes, 8, 235, 10.4239/wjd.v8.i6.235
Silverstein, 2005, Care of children and adolescents with type 1 diabetes: a statement of the American Diabetes Association, Diabetes Care, 28, 186, 10.2337/diacare.28.1.186
Klein, 1994, Relationship of hyperglycemia to the long-term incidence and progression of diabetic retinopathy, Arch Intern Med, 154, 2169, 10.1001/archinte.1994.00420190068008
Mohamed, 2013, Hyperglycemia as a risk factor for the development of retinopathy of prematurity, BMC Pediatr, 13, 78, 10.1186/1471-2431-13-78
Schrijvers, 2004, From hyperglycemia to diabetic kidney disease: the role of metabolic, hemodynamic, intracellular factors and growth factors/cytokines, Endocr Rev, 25, 971, 10.1210/er.2003-0018
Alicic, 2017, Diabetic kidney disease: challenges, progress, and possibilities, Clin J Am Soc Nephrol, 12, 2032, 10.2215/CJN.11491116
Selvin, 2004, Meta-analysis: glycosylated hemoglobin and cardiovascular disease in diabetes mellitus, Ann Intern Med, 141, 421, 10.7326/0003-4819-141-6-200409210-00007
Ormazabal, 2018, Association between insulin resistance and the development of cardiovascular disease, Cardiovasc Diabetol, 17, 122, 10.1186/s12933-018-0762-4
Turchin, 2009, Hypoglycemia and clinical outcomes in patients with diabetes hospitalized in the general ward, Diabetes Care, 32, 1153, 10.2337/dc08-2127
Nirantharakumar, 2012, Hypoglycaemia is associated with increased length of stay and mortality in people with diabetes who are hospitalized, Diabet Med, 29, e445, 10.1111/dme.12002
Akirov, 2017, Mortality among hospitalized patients with hypoglycemia: insulin related and noninsulin related, J Clin Endocrinol Metab, 102, 416, 10.1210/jc.2016-2653
Benjamin, 2002, Self-monitoring of blood glucose: the basics, Clin Diabetes, 20, 45, 10.2337/diaclin.20.1.45
Patton, 2012, Continuous Glucose Monitoring Versus Self-monitoring of Blood Glucose in Children with Type 1 Diabetes- Are there Pros and Cons for Both?, US Endocrinol, 8, 27, 10.17925/USE.2012.08.01.27
Bruen, 2017, Glucose sensing for diabetes monitoring: recent developments, Sensors (Switzerland), 17, 1866, 10.3390/s17081866
Cappon, 2017, Wearable continuous glucose monitoring sensors: a revolution in diabetes treatment, Electron, 6, 65, 10.3390/electronics6030065
Chen, 2017, Current and emerging technology for continuous glucose monitoring, Sensors (Switzerland), 17, 182, 10.3390/s17010182
Marvicsin, 2017, What Is New in Diabetes Technology?, J Nurse Pract, 13, 205, 10.1016/j.nurpra.2016.12.025
Acciaroli, 2018, Calibration of minimally invasive continuous glucose monitoring sensors: state-of-the-art and current perspectives, Biosensors, 8, 24, 10.3390/bios8010024
Oviedo, 2017, A review of personalized blood glucose prediction strategies for T1DM patients, Int J Numer Method Biomed Eng, 33, e2833, 10.1002/cnm.2833
Weng, 2016, Disease prediction with different types of neural network classifiers, Telemat Informatics, 33, 277, 10.1016/j.tele.2015.08.006
Zheng, 2017, A machine learning-based framework to identify type 2 diabetes through electronic health records, Int J Med Inform, 97, 120, 10.1016/j.ijmedinf.2016.09.014
Zhou, 2019, Text preprocessing for improving hypoglycemia detection from clinical notes – a case study of patients with diabetes, Int J Med Inform, 129, 374, 10.1016/j.ijmedinf.2019.06.020
Pappada, 2011, Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes, Diabetes Technol Ther, 13, 135, 10.1089/dia.2010.0104
Daskalaki, 2012, Real-Time adaptive models for the personalized prediction of glycemic profile in type 1 diabetes patients, Diabetes Technol Ther, 14, 168, 10.1089/dia.2011.0093
Bertachi, 2018, vol. 2148
Zarkogianni, 2015, Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring, Med Biol Eng Comput, 53, 1333, 10.1007/s11517-015-1320-9
Georga, 2013, Multivariate prediction of subcutaneous glucose concentration in type 1 diabetes patients based on support vector regression, IEEE J Biomed Heal Informatics, 17, 71, 10.1109/TITB.2012.2219876
Xie, 2018, vol. 2148
Georga, 2015, Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models, Med Biol Eng Comput, 53, 1305, 10.1007/s11517-015-1263-1
Midroni, 2018, vol. 2148
Sparacino, 2007, Glucose concentration can be predicted ahead in time from continuous glucose monitoring sensor time-series, IEEE Trans Biomed Eng, 54, 931, 10.1109/TBME.2006.889774
Pérez-Gandía, 2010, Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring, Diabetes Technol Ther, 12, 81, 10.1089/dia.2009.0076
Wang, 2013, A novel adaptive-weighted-average framework for blood glucose prediction, Diabetes Technol Ther, 15, 792, 10.1089/dia.2013.0104
Ben Ali, 2018, Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network, Biocybern Biomed Eng, 38, 828, 10.1016/j.bbe.2018.06.005
Hamdi, 2018, Accurate prediction of continuous blood glucose based on support vector regression and differential evolution algorithm, Biocybern Biomed Eng, 38, 362, 10.1016/j.bbe.2018.02.005
Xiang, 2016, A novel personalized diagnosis methodology using numerical simulation and an intelligent method to detect faults in a shaft, Appl Sci (Basel), 6, 414, 10.3390/app6120414
Park, 2018, Lired: A light-weight real-time fault detection system for edge computing using LSTM recurrent neural networks, Sensors (Switzerland), 18, 2110, 10.3390/s18072110
Zhang, 2020, A diagnosis method for the compound fault of gearboxes based on multi-feature and bp-adaboost, Symmetry (Basel), 12, 461, 10.3390/sym12030461
Alfian, 2019, False positive RFID detection using classification models, Appl Sci (Basel), 9, 1154, 10.3390/app9061154
Li, 2018, Comparison of feature learning methods for human activity recognition using wearable sensors, Sensors (Switzerland), 18, 679, 10.3390/s18020679
Rosati, 2018, Comparison of different sets of features for human activity recognition by wearable sensors, Sensors (Switzerland), 18, 4189, 10.3390/s18124189
Lima, 2019, Human activity recognition using inertial sensors in a smartphone: an overview, Sensors (Switzerland), 19, 3213, 10.3390/s19143213
Ahmed, 2020, Enhanced human activity recognition based on smartphone sensor data using hybrid feature selection model, Sensors (Switzerland), 20, 317, 10.3390/s20010317
Zhu, 2017, Occupancy estimation with environmental sensing via non-iterative LRF feature learning in time and frequency domains, Energy Build, 141, 125, 10.1016/j.enbuild.2017.01.057
Turksoy, 2013, Hypoglycemia early alarm systems based on multivariable models, Ind Eng Chem Res, 52, 12329, 10.1021/ie3034015
Alfian, 2020, Blood Glucose Prediction Model for Type 1 Diabetes based on Extreme Gradient Boosting, IOP Conf Ser Mater Sci Eng, 803, 10.1088/1757-899X/803/1/012012
Han, 2012
Rumelhart, 1986, Learning representations by back-propagating errors, Nature, 323, 533, 10.1038/323533a0
DirecNet Dataset. Evaluation of Counter-regulatory Hormone Responses during Hypoglycemia and the Accuracy of Continuous Glucose Monitors in Children with T1DM. https://public.jaeb.org/direcnet/stdy/167.
Savitzky, 1964, Smoothing and differentiation of data by simplified least squares procedures, Anal Chem, 36, 1627, 10.1021/ac60214a047
Del, 2012, A glucose-specific metric to assess predictors and identify models, IEEE Trans Biomed Eng, 59, 1281, 10.1109/TBME.2012.2185234