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Measuring Glycemic Variability and Predicting Blood Glucose Levels Using Machine Learning Regression Models

Struble, Nigel
http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1382664092

Abstract Details

2013, Master of Science (MS), Ohio University, Computer Science (Engineering and Technology).
This thesis presents research in machine learning for diabetes management. There are two major contributions:(1) development of a metric for measuring glycemic variability, a serious problem for patients with diabetes; and (2) predicting patient blood glucose levels, in order to preemptively detect and avoid potential health problems. The glycemic variability metric uses machine learning trained on multiple statistical and domain specific features to match physician consensus of glycemic variability. The metric performs similarly to an individual physician's ability to match the consensus. When used as a screen for detecting excessive glycemic variability, the metric outperforms the baseline metrics. The blood glucose prediction model uses machine learning to integrate a general physiological model and life-events to make patient-specific predictions 30 and 60 minutes in the future. The blood glucose prediction model was evaluated in several situations such as near a meal or during exercise. The prediction model outperformed the baselines prediction models, and performed similarly to, and in some cases outperformed, expert physicians who were given the same prediction problems.
Cynthia Marling (Advisor)
Razvan Bunescu (Committee Member)
Frank Schwartz (Committee Member)
Jundong Liu (Committee Member)
108 p.
Computer Science
Machine Learning; Glycemic Variability; BGL Prediction; Diabetes

Recommended Citations

Citations

  • Struble, N. (2013). Measuring Glycemic Variability and Predicting Blood Glucose Levels Using Machine Learning Regression Models [Master's thesis, Ohio University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1382664092

    APA Style (7th edition)

  • Struble, Nigel. Measuring Glycemic Variability and Predicting Blood Glucose Levels Using Machine Learning Regression Models. 2013. Ohio University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1382664092.

    MLA Style (8th edition)

  • Struble, Nigel. "Measuring Glycemic Variability and Predicting Blood Glucose Levels Using Machine Learning Regression Models." Master's thesis, Ohio University, 2013. http://rave.ohiolink.edu/etdc/view?acc_num=ohiou1382664092

    Chicago Manual of Style (17th edition)

ohiou1382664092
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© 2013, all rights reserved.
This open access ETD is published by Ohio University and OhioLINK.