Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention

Chloe Smith, Maxfield Kouzel, Xugui Zhou, Homa Alemzadeh
3/16/2023
cs.LG

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Abstract

Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in the timely treatment of unsafe blood glucose (BG) levels, especially in the case of rebound hyperglycemia. We propose a machine learning (ML) method for predictive BG scenario categorization that outputs messages alerting the patient to upcoming BG trends to allow for earlier, educated treatment. In addition to standard notifications of predicted hypoglycemia and hyperglycemia, we introduce BG scenario-specific alert messages and the preliminary steps toward precise basal suggestions for the prevention of rebound hyperglycemia. Experimental evaluation on the DCLP3 clinical dataset achieves >98% accuracy and >79% precision for predicting rebound high events for patient alerts.

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Cite this paper

@article{smith2023short,
  title  = {Short: Basal-Adjust: Trend Prediction Alerts and Adjusted Basal Rates for Hyperglycemia Prevention},
  author = {Chloe Smith and Maxfield Kouzel and Xugui Zhou and Homa Alemzadeh},
  year   = {2023},
  eprint = {2303.09913},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2303.09913v1}
}

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