Tutorial: LSTM and TSFresh Pipeline Built to Predict Hypoglycemia 30 Minutes Early

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A technical tutorial published on DEV Community outlines how to build a machine learning pipeline for real-time Continuous Glucose Monitoring (CGM) analytics. The system combines LSTM neural networks and TSFresh feature extraction to predict hypoglycemia risk up to 30 minutes before it occurs. It uses InfluxDB for time-series data storage and TensorFlow or PyTorch as the deep learning backend. The pipeline processes high-frequency glucose readings from devices like Dexcom or Abbott Libre, extracting statistical features such as velocity and acceleration of glucose curves. The goal is to reduce alarm fatigue and enable closed-loop alerts or automated insulin adjustments based on anomaly scores.

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