Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network

TL;DR

This paper presents a deep neural network combining LSTM and Bi-LSTM layers to accurately predict future blood glucose levels, enabling proactive management for diabetes patients.

Contribution

It introduces a novel deep learning model with LSTM and Bi-LSTM layers for blood glucose prediction, outperforming baseline methods on real patient data.

Findings

Outperforms baseline methods in prediction accuracy

Effective across multiple prediction horizons

Validated on 26 datasets from 20 patients

Abstract

A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.