Explainable machine learning for real-time hypoglycaemia and hyperglycaemia prediction and personalised control recommendations

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Abstract

Background: the occurrences of acute complications arising from hypoglycaemia and hyperglycaemia peak as young adults with type 1 diabetes (T1D) take control of their own care. Continuous glucose monitoring (CGM) devices provide real-time blood glucose readings enabling users to manage their control pro-actively. Machine learning algorithms can use CGM data to make ahead-of-time risk predictions and provide insight into an individual’s longer-term control.

Methods: we introduce explainable machine learning to make predictions of hypoglycaemia (<70mg/dL) and hyperglycaemia (>270mg/dL) 60 minutes ahead-of-time. We train our models using CGM data from 153 people living with T1D in the CITY survey totalling over 28000 days of usage, which we summarise into (short-term, medium-term, and long-term) blood glucose features along with demographic information. We use machine learning explanations (SHAP) to identify which features have been most important in predicting risk per user.

Results: machine learning models (XGBoost) show excellent performance at predicting hypoglycaemia (AUROC: 0.998) and hyperglycaemia (AUROC: 0.989) in comparison to a baseline heuristic and logistic regression model.

Conclusions: maximising model performance for blood glucose risk prediction and management is crucial to reduce the burden of alarm-fatigue on CGM users. Machine learning enables more precise and timely predictions in comparison to baseline models. SHAP helps identify what about a CGM user’s blood glucose control has led to predictions of risk which can be used to reduce their long-term risk of complications.

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Contributors

Author: Christopher Duckworth

Author: Matthew J. Guy

Author: Adriane Chapman

Author: Michael Boniface

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