Inducing Causal Structure for Interpretable Neural Networks Applied to Glucose Prediction for T1DM Patients
This work addresses the challenge of integrating expert causal knowledge into neural networks for healthcare applications, specifically glucose prediction, though it is incremental as it builds on existing IIT techniques.
The study applied Interchange Intervention Training (IIT) to a neural network for predicting blood glucose levels in Type 1 Diabetes Mellitus patients, showing that the IIT-trained model outperformed a standard model across different prediction horizons.
Causal abstraction techniques such as Interchange Intervention Training (IIT) have been proposed to infuse neural network with expert knowledge encoded in causal models, but their application to real-world problems remains limited. This article explores the application of IIT in predicting blood glucose levels in Type 1 Diabetes Mellitus (T1DM) patients. The study utilizes an acyclic version of the simglucose simulator approved by the FDA to train a Multi-Layer Perceptron (MLP) model, employing IIT to impose causal relationships. Results show that the model trained with IIT effectively abstracted the causal structure and outperformed the standard one in terms of predictive performance across different prediction horizons (PHs) post-meal. Furthermore, the breakdown of the counterfactual loss can be leveraged to explain which part of the causal mechanism are more or less effectively captured by the model. These preliminary results suggest the potential of IIT in enhancing predictive models in healthcare by effectively complying with expert knowledge.