A personalized and interpretable deep learning based approach to predict blood glucose concentration in type 1 diabetes

The management of type 1 diabetes mellitus (T1DM) is a burdensome life-long task. In fact, T1DM individuals are request to perform every day tens of actions to adapt the insulin therapy, aimed at maintaining the blood glucose (BG) concentration as much as possible into a safe range coping with the day-to-day variability of their life style. The recent availability of continuous glucose monitoring (CGM) devices and other low-cost wearable sensors to track important vital and activity signals, is stimulating the development of decision support systems to lower this burden. Modern deep learning models, trained using rich amount of information, are a suitable and effective instrument for such purpose, especially if used to predict future BG values. However, the high accuracy of deep learning approaches is often obtained at the expense of less interpretability. To surpass this limit, in this work we propose a new deep learning method for BG prediction based on a personalized bidirectional long short-term memory (LSTM) equipped with a tool that enables its interpretability. The OhioT1DM Dataset was used to develop a model targeting future BG at 30 and 60 minute prediction horizons (PH). The accuracy of model predictions was evaluated in terms of root mean square error (RMSE), mean absolute error (MAE), and the time gained (TG) to anticipate the actual glucose concentration. The obtained results show fairly good prediction accuracy (for PH = 30/60 min): RMSE = 20.20/34.19 mg/dl, MAE = 14.74/25.98 mg/dl, and TG = 9.17/18.33 min. Moreover, we showed, in a representative case, that our algorithm is able to preserve the physiological meaning of the considered inputs. In conclusion, we built a model able to provide reliable glucose performance ensuring the interpretability of its output. Future work will assess model performance against other competitive strategies.