Automatic detection of excessive glycemic variability for diabetes management

Proceedings - 10th International Conference on Machine Learning and Applications, ICMLA 2011

Volumn 2, Issue , 2011, Pages 148-154

  Wiley, Matthew ^a   Bunescu, Razvan ^a   Marling, Cindy ^a   Shubrook, Jay ^b   Schwartz, Frank ^b  

^a US Institute of Navigation   (United States)

^b OHIO UNIVERSITY   (United States)

Author keywords

classification; diabetes; glycemic variability

Indexed keywords

AUTOMATIC DETECTION; AUTOMATIC FEATURE SELECTION; BLOOD GLUCOSE LEVEL; CONTINUOUS GLUCOSE MONITORING; CUBIC SPLINE; DATA SETS; DIABETES MANAGEMENT; EMPIRICAL EVALUATIONS; GLYCEMIC VARIABILITY; IMPACT OF NOISE; NAIVE BAYES; RECOGNITION FEATURES; STATE OF THE ART; SUPPORT VECTOR MACHINE (SVM); VARIABILITY MODEL;

CLASSIFICATION (OF INFORMATION); GLUCOSE; MEDICAL PROBLEMS; PATTERN RECOGNITION;

SUPPORT VECTOR MACHINES;

EID: 84857813854     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICMLA.2011.39     Document Type: Conference Paper

References (22)

1. World Health Organization, "Diabetes," 2011, http://www.who.int/mediacentre/factsheets/fs312/en/index.html, accessed April, 2011.
2. Diabetes Control and Complications Trial Research Group, "The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus," New England Journal of Medicine, vol. 329, no. 14, pp. 977-986, 1993.
3. A. Ceriello and M. A. Ihnat, "'Glycaemic variability': A new therapeutic challenge in diabetes and the critical care setting," Diabetic Medicine, vol. 27, no. 8, pp. 862-867, 2010.
4. I. B. Hirsch and M. Brownlee, "Should minimal blood glucose variability become the gold standard of glycemic control?" Journal of Diabetes and its Complications, vol. 19, no. 3, pp. 178-181, 2005. (Pubitemid 40615551)
5. E. S. Kilpatrick, A. S. Rigby, and S. L. Atkin, "For debate. Glucose variability and diabetes complication risk: We need to know the answer,"Diabetic Medicine, vol. 27, no. 8, pp. 868-871, 2010.
6. -, "The effect of glucose variability on the risk of microvascular complications in type 1 diabetes." Diabetes Care, vol. 29, no. 7, pp. 1486-1490, 2006.
7. L. Monnier and C. Colette, "Glycemic variability," Diabetes Care, vol. 31, no. s2, pp. S-150-S-154, 2008.
8. L. Monnier, E. Mas, C. Ginet, F. Michel, L. Villon, J. Cristol, and C. Colette, "Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes," Journal of the American Medical Association, vol. 295, no. 14, pp. 1681-1687, 2006.
9. D. Rodbard, "Interpretation of continuous glucose monitoring data: Glycemic variability and quality of glycemic control," Diabetes Technology & Therapeutics, vol. 11, no. s1, pp. S-55-S-67, 2009.
10. C. R. Marling, J. H. Shubrook, S. J. Vernier, M. T. Wiley, and F. L. Schwartz, "Characterizing blood glucose variability using new metrics with continuous glucose monitoring data," Diabetes Science and Technology, vol. 5, no. 4, pp. 871-878, 2011.
11. F. J. Service, G. D. Molnar, J. W. Rosevear, E. Ackerman, L. C. Gatewood, and W. Taylor, "Mean amplitude of glycemic excursions, a measure of diabetic instability," Diabetes, vol. 19, no. 9, pp. 644-655, 1970.
12. B. W. Bequette, "Continuous glucose monitoring: Real-time algorithms for calibration, filtering, and alarms," Diabetes Science and Technology, vol. 4, no. 2, pp. 404-418, 2010.
13. G. Sparacino, A. Facchinetti, A. Maran, and C. Cobelli, "Continuous glucose monitoring time series and hypo/hyperglycemia prevention: Requirements, methods, open problems," Current Diabetes Reviews, vol. 4, pp. 181-192, 2008.
14. J. Mastrototaro, J. Shin, A. Marcus, and G. Sulur, "The accuracy and efficacy of real-time continuous glucose monitoring sensor in patients with type 1 diabetes," Diabetes Technology & Therapeutics, vol. 10, no. 5, pp. 385-390, 2008.
15. S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th ed. Amsterdam: Elsevier, 2009.
16. C. M. Bishop, Neural Networks for Pattern Recognition. New York: Oxford University Press, 1995.
17. B. Schölkopf and A. J. Smola, Learning with kernels - support vector machines, regularization, optimization and beyond. Cambridge, MA: MIT Press, 2002.
18. V. N. Vapnik, The Nature of Statistical Learning Theory. Berlin: Springer-Verlag, 1995.
19. K. Hornik, M. Stinchcombe, and H. White, "Multilayer feedforward networks are universal approximators," Neural Networks, vol. 2, no. 5, pp. 359-366, 1989. (Pubitemid 20609008)
20. I. H. Witten, E. Frank, L. Trigg, M. Hall, G. Holmes, and S. J. Cunningham, "Weka: Practical machine learning tools and techniques with Java implementations," in Proceedings ICONIP/ANZIIS/ANNES99: Future Directions for Intelligent Systems and Information Sciences, 1999, pp. 192-196.
21. C.-C. Chang and C.-J. Lin, LIBSVM: A library for support vector machines, 2001, software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm, accessed April, 2011.
22. W. Chu and S. S. Keerthi, "New approaches to support vector ordinal regression," in Proceedings of the 22nd International Conference on Machine Learning. New York, NY, USA: ACM, 2005, pp. 145-152.