Predictive analytics for blood glucose concentration: an empirical study using the tree-based ensemble approach
暂无分享,去创建一个
Jiaming Liu | Linan Zhang | Zeming Zhang | Sicheng Zhang | Liuan Wang | Liuan Wang | Linan Zhang | Sicheng Zhang | Jiaming Liu | Zeming Zhang
[1] J. Shaw,et al. IDF Diabetes Atlas: Global estimates of diabetes prevalence for 2017 and projections for 2045. , 2018, Diabetes research and clinical practice.
[2] J. Ross Quinlan,et al. Induction of Decision Trees , 1986, Machine Learning.
[3] Jamie L McConaha,et al. Barriers and facilitators to diabetes self‐management in a primary care setting – Patient perspectives , 2019, Research in social & administrative pharmacy : RSAP.
[4] Giovanni Sparacino,et al. Diabetes: Models, Signals, and Control , 2009 .
[5] Nongyao Nai-arun,et al. Comparison of Classifiers for the Risk of Diabetes Prediction , 2015 .
[6] Yan Li,et al. A distributed ensemble approach for mining healthcare data under privacy constraints , 2016, Inf. Sci..
[7] P. Olson,et al. Patients' and healthcare providers' perspectives on diabetes management: A systematic review of qualitative studies. , 2020, Research in social & administrative pharmacy : RSAP.
[8] E. K. Choi,et al. Healthcare transition readiness, family support, and self-management competency in Korean emerging adults with Type 1 diabetes mellitus. , 2019, Journal of pediatric nursing.
[9] Rolf Johansson,et al. Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. , 2009, Mathematical biosciences.
[10] Dimitrios I. Fotiadis,et al. Multivariate Prediction of Subcutaneous Glucose Concentration in Type 1 Diabetes Patients Based on Support Vector Regression , 2013, IEEE Journal of Biomedical and Health Informatics.
[11] A. Hergenroeder,et al. Development of a group-based, peer-mentor intervention to promote disease self-management skills among youth with chronic medical conditions. , 2019, Journal of pediatric nursing.
[12] Walter Gall,et al. A Diabetes Self-Management Prototype in an AAL-Environment to Detect Remarkable Health States , 2016, eHealth.
[13] Konstantina S. Nikita,et al. SMARTDIAB: A Communication and Information Technology Approach for the Intelligent Monitoring, Management and Follow-up of Type 1 Diabetes Patients , 2010, IEEE Transactions on Information Technology in Biomedicine.
[14] Yizeng Liang,et al. Exploring the relationship between 5'AMP-activated protein kinase and markers related to type 2 diabetes mellitus. , 2013, Talanta.
[15] Yoav Freund,et al. A decision-theoretic generalization of on-line learning and an application to boosting , 1997, EuroCOLT.
[16] Scott M. Pappada,et al. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes. , 2011, Diabetes technology & therapeutics.
[17] Hrushikesh N. Mhaskar,et al. A Deep Learning Approach to Diabetic Blood Glucose Prediction , 2017, Front. Appl. Math. Stat..
[18] A. Rogers,et al. Social support and self-management capabilities in diabetes patients: An international observational study. , 2016, Patient education and counseling.
[19] Majid Sarrafzadeh,et al. A flexible data-driven comorbidity feature extraction framework , 2016, Comput. Biol. Medicine.
[20] Jaouher Ben Ali,et al. Continuous blood glucose level prediction of Type 1 Diabetes based on Artificial Neural Network , 2018 .
[21] Sharvari Chandrashekhar Tamane,et al. A Comparative Analysis on the Evaluation of Classification Algorithms in the Prediction of Diabetes , 2018, International Journal of Electrical and Computer Engineering (IJECE).
[22] Diana Lungeanu,et al. Chaotic time series prediction for glucose dynamics in type 1 diabetes mellitus using regime-switching models , 2017, Scientific Reports.
[23] Peihua Chen,et al. Diabetes classification model based on boosting algorithms , 2018, BMC Bioinformatics.
[24] Jignesh R. Parikh,et al. Reverse Engineering and Evaluation of Prediction Models for Progression to Type 2 Diabetes , 2016, Journal of diabetes science and technology.
[25] Claudio Cobelli,et al. A System Model of Oral Glucose Absorption: Validation on Gold Standard Data , 2006, IEEE Transactions on Biomedical Engineering.
[26] Arif Gülten,et al. Classifier ensemble construction with rotation forest to improve medical diagnosis performance of machine learning algorithms , 2011, Comput. Methods Programs Biomed..
[27] C. Cobelli,et al. Artificial neural network algorithm for online glucose prediction from continuous glucose monitoring. , 2010, Diabetes technology & therapeutics.
[28] Dimitrios I. Fotiadis,et al. Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models , 2015, Medical & Biological Engineering & Computing.
[29] Karim Keshavjee,et al. Performance Analysis of Data Mining Classification Techniques to Predict Diabetes , 2016 .
[30] Giuseppe De Nicolao,et al. Neural Network Incorporating Meal Information Improves Accuracy of Short-Time Prediction of Glucose Concentration , 2012, IEEE Transactions on Biomedical Engineering.
[31] Anchaleeporn Amatayakul,et al. Predictors of diabetes self-management among type 2 diabetics in Indonesia: Application theory of the health promotion model , 2017, International journal of nursing sciences.
[32] Andrew Stranieri,et al. Data analytics identify glycated haemoglobin co-markers for type 2 diabetes mellitus diagnosis , 2016, Comput. Biol. Medicine.
[33] Kagan Tumer,et al. Error Correlation and Error Reduction in Ensemble Classifiers , 1996, Connect. Sci..
[34] E. Renard,et al. Real-time continuous glucose monitoring (CGM) integrated into the treatment of type 1 diabetes: consensus of experts from SFD, EVADIAC and SFE. , 2012, Diabetes & metabolism.
[35] J. Friedman. Greedy function approximation: A gradient boosting machine. , 2001 .
[36] Da Tao,et al. Does the use of consumer health information technology improve outcomes in the patient self-management of diabetes? A meta-analysis and narrative review of randomized controlled trials , 2014, Int. J. Medical Informatics.
[37] Usman Qamar,et al. IntelliHealth: A medical decision support application using a novel weighted multi-layer classifier ensemble framework , 2016, J. Biomed. Informatics.
[38] Leo Breiman,et al. Random Forests , 2001, Machine Learning.