Explainable AI-based Decision Support for Nocturnal Hypoglycemia Prevention in Type 1 Diabetes

Abstract

Purpose: Nocturnal hypoglycemia (NH) remains a challenge for individuals with type 1 diabetes (T1D), particularly those who are physically active or on multiple daily injections (MDI). We leveraged an explainable evidential neural network model that forecasts minimum overnight glucose to identify NH risk factors and generate recommendations for NH prevention. Methods: We analyzed the impact of glucose and physical activity (PA) factors on predicted NH probability using SHapley Additive exPlanations (SHAP). Data were from 20 adults with T1D on MDI (10 females; mean age 39 years; HbA1c 7\%) who participated in a cross-over study (NCT05967260). Results: A total of 502 nights were analyzed. Bedtime glucose was the strongest predictor of NH. Other relevant factors included recent exposure to high or low glucose, glucose variability before bedtime, and timing of PA. Some associations appeared physiologically counterintuitive, possibly reflecting behavioral adjustments. Based on the identified risk factors and their SHAP values, we determined key decision points and developed recommendations that may help prevent NH, such as consuming a bedtime snack or discussing potential adjustments to insulin therapy with a healthcare provider. Conclusion: Identifying predictors of NH offers insights for managing NH risk in clinical decision support.