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

Source: [Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network](<https://www.sota2.com/research/paper/68fa5ee21e209131ce9e60a5>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T19:02:51.915Z

Authors: Qingnan Sun, Marko V. Jankovic, Lia Bally, Stavroula G. Mougiakakou

Published: 2018-09-11T12:26:46.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

- [Glucose Forecasting on OhioT1DM PH = 60 minutes](<https://www.sota2.com/research/sota/glucose-forecasting-on-ohiot1dm-ph-60-minutes.md>): Glucose forecasting; OhioT1DM PH = 60 minutes; RMSE (PH=60min): 33.44.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1809.03817>)

[PDF](<https://arxiv.org/pdf/1809.03817.pdf>)