New physics-LSTM hybrid models for control-oriented glucose prediction in Type 1 Diabetes*
Abstract
Glucose modelling is crucial for an efficient management of Type 1 Diabetes (T1D). In recent years Neural Networks (NNs) techniques, in particular Long Short-Term Memory networks (LSTMs), have shown very promising performances in glucose prediction. However, to use them in control application, these NNs have to be able to correctly learn the effect of each single input from the data. Considering that meals and insulin boluses characterizing the datasets occur simultaneously, the pure NNs are often unable to distinguish their effects. This work proposes two Physics-LSTM hybrid Models (PhLSTMs) merging a classical Black Box model (BB) with two LSTM-based approaches in order to obtain satisfactory prediction capabilities for single impulse responses to insulin/meal. The resulting PhLSTMs have been tested on a case study of 2 in silico patients showing satisfactory glycemic prediction performances both on real-life datasets and impulse responses to insulin/meal.
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