From Reactive to Predictive: Machine Learning Forecasts Glucose Spikes

This article demonstrates how Transformer-based deep learning can shift glucose monitoring from reactive crisis management to proactive prevention by predicting spikes 30 minutes in advance. For CGM users, this represents a fundamental shift toward personalized health management—moving from after-the-fact detection to preventive action. The technique showcases how advanced time-series forecasting extracts meaningful health insights from wearable sensor data, potentially transforming diabetes management and metabolic health.