Interchange Intervention Training Applied to Post-meal Glucose Prediction for Type 1 Diabetes Mellitus Patients
Keywords: interchange intervention, causality, glucose prediction, interpretability, diabetes
TL;DR: IIT enhances blood glucose prediction in Type 1 Diabetes patients by effectively abstracting causal relationships in a MLP model, outperforming standard models across four post-meal prediction horizons using an acyclic FDA-approved simulator.
Abstract: This research explores the application of Interchange Intervention Training (IIT) in predicting blood glucose levels in Type 1 Diabetes Mellitus (T1DM) patients by leveraging expert knowledge encoded in causal models. 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 abstract its causal internal structure. Results show that the model trained with IIT effectively abstracted the causal structure and it outperformed the standardly trained one in terms of predictive performance across different prediction horizons (PHs) post-meal. This technique also allows us to measure the extent to which the causal structure has been abstracted, promoting the interpretability of the black-box model. These preliminary results with the acyclic model suggest the potential of IIT in enhancing predictive models in healthcare by effectively complying with expert knowledge.
Submission Number: 21
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