Beschreibung
This work 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 physicians 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.
Autorenporträt
Master's degree in Computer Science focusing on Artificial Intelligence from Ohio University (2013), Bachelor's degree in Computer Science from Ohio University (2011).
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OmniScriptum SRL
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