Tailoring Adverse Event Prediction in Type 1 Diabetes with Patient-Specific Deep Learning Models

TL;DR

This paper introduces a personalized deep learning approach for blood glucose prediction in Type 1 Diabetes, improving accuracy and responsiveness by tailoring models to individual patient data, which enhances automated insulin delivery and decision support.

Contribution

It presents a novel patient-specific deep learning framework that outperforms traditional models, including strategies for effective personalization with limited data in real-world settings.

Findings

Personalized models significantly improve adverse event prediction.

Fine-tuning enhances model performance over leave-one-subject-out methods.

Effective personalization is achievable with limited patient data.

Abstract

Effective management of Type 1 Diabetes requires continuous glucose monitoring and precise insulin adjustments to prevent hyperglycemia and hypoglycemia. With the growing adoption of wearable glucose monitors and mobile health applications, accurate blood glucose prediction is essential for enhancing automated insulin delivery and decision-support systems. This paper presents a deep learning-based approach for personalized blood glucose prediction, leveraging patient-specific data to improve prediction accuracy and responsiveness in real-world scenarios. Unlike traditional generalized models, our method accounts for individual variability, enabling more effective subject-specific predictions. We compare Leave-One-Subject-Out Cross-Validation with a fine-tuning strategy to evaluate their ability to model patient-specific dynamics. Results show that personalized models significantly improve the prediction of adverse events, enabling more precise and timely interventions in real-world scenarios. To assess the impact of patient-specific data, we conduct experiments comparing a multimodal, patient-specific approach against traditional CGM-only methods. Additionally, we perform an ablation study to investigate model performance with progressively smaller training sets, identifying the minimum data required for effective personalization-an essential consideration for real-world applications where extensive data collection is often challenging. Our findings underscore the potential of adaptive, personalized glucose prediction models for advancing next-generation diabetes management, particularly in wearable and mobile health platforms, enhancing consumer-oriented diabetes care solutions.