# Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction

arXiv: https://arxiv.org/abs/2101.06850 · HTML: https://mlsanity.com/paper/2101.06850/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 66 references that are in the map.
- First posted on arXiv 18 Jan 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Learning in Neural Networks: An Overview](https://mlsanity.com/paper/1404.7828/) (2014)
- [Speech Recognition with Deep Recurrent Neural Networks](https://mlsanity.com/paper/1303.5778/) (2013)
- [How to Construct Deep Recurrent Neural Networks](https://mlsanity.com/paper/1312.6026/) (2013)
- [Convolutional Recurrent Neural Networks for Glucose Prediction](https://mlsanity.com/paper/1807.03043/) (2018)
- [Deep Multi-Output Forecasting: Learning to Accurately Predict Blood Glucose Trajectories](https://mlsanity.com/paper/1806.05357/) (2018)
- [Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network](https://mlsanity.com/paper/1809.03817/) (2018)

## Built on by (most cited first, 8 of 8)

- [Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting](https://mlsanity.com/paper/2411.10703/) (2024)
- [A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction](https://mlsanity.com/paper/2505.08821/) (2025)
- [Global Deep Forecasting with Patient-Specific Pharmacokinetics](https://mlsanity.com/paper/2309.13135/) (2023)
- [DeepFakes: Detecting Forged and Synthetic Media Content Using Machine Learning](https://mlsanity.com/paper/2109.02874/) (2021)
- [Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data](https://mlsanity.com/paper/2302.12856/) (2023)
- [Application of Long Short-Term Memory Recurrent Neural Networks Based on the BAT-MCS for Binary-State Network Approximated Time-Dependent Reliability Problems](https://mlsanity.com/paper/2202.07837/) (2022)
- [MATURE-HEALTH: HEALTH Recommender System for MAndatory FeaTURE choices](https://mlsanity.com/paper/2304.09099/) (2023)
- [Blood Glucose Control Via Pre-trained Counterfactual Invertible Neural Networks](https://mlsanity.com/paper/2405.17458/) (2024)

## Abstract (arXiv)

Blood glucose (BG) management is crucial for type-1 diabetes patients resulting in the necessity of reliable artificial pancreas or insulin infusion systems. In recent years, deep learning techniques have been utilized for a more accurate BG level prediction system. However, continuous glucose monitoring (CGM) readings are susceptible to sensor errors. As a result, inaccurate CGM readings would affect BG prediction and make it unreliable, even if the most optimal machine learning model is used. In this work, we propose a novel approach to predicting blood glucose level with a stacked Long short-term memory (LSTM) based deep recurrent neural network (RNN) model considering sensor fault. We use the Kalman smoothing technique for the correction of the inaccurate CGM readings due to sensor error. For the OhioT1DM dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 minutes and 60 minutes of prediction horizon (PH), respectively. To the best of our knowledge, this is the leading average prediction accuracy for the ohioT1DM dataset. Different physiological information, e.g., Kalman smoothed CGM data, carbohydrates from the meal, bolus insulin, and cumulative step counts in a fixed time interval, are crafted to represent meaningful features used as input to the model. The goal of our approach is to lower the difference between the predicted CGM values and the fingerstick blood glucose readings - the ground truth. Our results indicate that the proposed approach is feasible for more reliable BG forecasting that might improve the performance of the artificial pancreas and insulin infusion system for T1D diabetes management.