ArticleBMC medical informatics and decision making2021
Stacked LSTM based deep recurrent neural network with kalman smoothing for blood glucose prediction.
Article in BMC medical informatics and decision making, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 2 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 2 syntheses or guidelines pooled it, 160 citations in OpenAlex.
- Pooled it
- Internet of Things in Pregnancy Care Coordination and Management: A Systematic Review.Sensors (Basel, Switzerland) · 2023Pooled it
- Deep learning for blood glucose prediction: Reproducibility challenges and factors affecting differential performance.PLOS digital health · 2026Article
- Trustworthy AI for personalized glycemic control: a systematic review and critical appraisal of multimodal forecasting and safety-critical closed-loop control.Reviews in endocrine & metabolic disorders · 2026Review
- AI-Assisted Molecular Biosensors: Design Strategies for Wearable and Real-Time Monitoring.International journal of molecular sciences · 2026Review
- HEAL: health-enhanced adaptive LoRaWAN for AI-based real-time soldier monitoring and status prediction.Scientific reports · 2026Article
- Future-aware blood glucose forecasting using knowledge distillation with transformer-based sequence-to-sequence models.Scientific reports · 2026Article
- Reducing bias and enhancing equity in AI-enabled precision nutrition: addressing measurement error across wearables, multiomics, and dietary data.Frontiers in digital health · 2026Review
- Observational
- A Comparative Study of Transformer-Based Models for Multi-Horizon Blood Glucose Prediction.ArXiv · 2025Article
- Personalized Blood Glucose Forecasting From Limited CGM Data Using Incrementally Retrained LSTM.IEEE transactions on bio-medical engineering · 2025Article
- A perspective on harmonizing diabetes management datasets.Data in brief · 2025Article
- Optimizing hypoglycaemia prediction in type 1 diabetes with Ensemble Machine Learning modeling.BMC medical informatics and decision making · 2025Observational
- Intelligent glucose management in hospitalized patients: Short-term glucose and adverse events prediction.PloS one · 2025Article
- Assessing Locomotive Syndrome Through Instrumented Five-Time Sit-to-Stand Test and Machine Learning.Sensors (Basel, Switzerland) · 2024Article
- Hybrid CNN-GRU Model for Real-Time Blood Glucose Forecasting: Enhancing IoT-Based Diabetes Management with AI.Sensors (Basel, Switzerland) · 2024Article
- Learning control-ready forecasters for Blood Glucose Management.Computers in biology and medicine · 2024Article
- Machine Learning and Deep Learning Models for Nocturnal High- and Low-Glucose Prediction in Adults with Type 1 Diabetes.Diagnostics (Basel, Switzerland) · 2024Article
- Article
- Deep Learning-Based Glucose Prediction Models: A Guide for Practitioners and a Curated Dataset for Improved Diabetes Management.IEEE open journal of engineering in medicine and biology · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundBlood 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.
methodsIn 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.
resultsFor the OhioT1DM (2018) dataset, containing eight weeks' data from six different patients, we achieve an average RMSE of 6.45 and 17.24 mg/dl for 30 min and 60 min of prediction horizon (PH), respectively.
conclusionsTo 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.