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

• URL: http://arxiv.org/abs/2101.06850v1
• Date: Mon, 18 Jan 2021 02:31:38 GMT
• Title: Stacked LSTM Based Deep Recurrent Neural Network with Kalman Smoothing for Blood Glucose Prediction
• Authors: Md Fazle Rabby, Yazhou Tu, Md Imran Hossen, Insup Le, Anthony S Maida, Xiali Hei
• Abstract summary: 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. 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) 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.
• Score: 4.040272012640556
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

Related papers

• Type 1 Diabetes Management using GLIMMER: Glucose Level Indicator Model with Modified Error Rate [6.300322064585917]
  We develop GLIMMER, a machine learning approach for forecasting blood glucose levels. GLIMMER categorizes glucose values into normal and abnormal ranges and devises a novel custom loss function to prioritize accuracy in dysglycemic events.
  arXiv  Detail & Related papers  (2025-02-20T01:26:00Z)
• AttenGluco: Multimodal Transformer-Based Blood Glucose Forecasting on AI-READI Dataset [8.063401183752347]
  Diabetes is a chronic metabolic disorder characterized by persistently high blood glucose levels (BGLs) Recent deep learning models show promise in improving BGL prediction. We propose AttenGluco, a multimodal Transformer-based framework for long-term blood glucose prediction.
  arXiv  Detail & Related papers  (2025-02-14T05:07:38Z)
• Finetuning and Quantization of EEG-Based Foundational BioSignal Models on ECG and PPG Data for Blood Pressure Estimation [53.2981100111204]
  Photoplethysmography and electrocardiography can potentially enable continuous blood pressure (BP) monitoring. Yet accurate and robust machine learning (ML) models remains challenging due to variability in data quality and patient-specific factors. In this work, we investigate whether a model pre-trained on one modality can effectively be exploited to improve the accuracy of a different signal type. Our approach achieves near state-of-the-art accuracy for diastolic BP and surpasses by 1.5x the accuracy of prior works for systolic BP.
  arXiv  Detail & Related papers  (2025-02-10T13:33:12Z)
• Synthetic Time Series Data Generation for Healthcare Applications: A PCG Case Study [43.28613210217385]
  We employ and compare three state-of-the-art generative models to generate PCG data. Our results demonstrate that the generated PCG data closely resembles the original datasets. In our future work, we plan to incorporate this method into a data augmentation pipeline to synthesize abnormal PCG signals with heart murmurs.
  arXiv  Detail & Related papers  (2024-12-17T18:07:40Z)
• Let Curves Speak: A Continuous Glucose Monitor based Large Sensor Foundation Model for Diabetes Management [3.8195320624847833]
  Integrating AI with continuous glucose monitoring holds promise for near-future glucose prediction. CGM-LSM is pretrained on 15.96 million glucose records from 592 diabetes patients for near-future glucose prediction. LSM achieved exceptional performance, with an rMSE of 29.81 mg/dL for type 1 diabetes patients and 23.49 mg/dL for type 2 diabetes patients in a two-hour prediction horizon.
  arXiv  Detail & Related papers  (2024-12-12T21:35:13Z)
• Hybrid Attention Model Using Feature Decomposition and Knowledge Distillation for Glucose Forecasting [6.466206145151128]
  GlucoNet is an AI-powered sensor system for continuously monitoring behavioral and physiological health. We propose a decomposition-based transformer model that incorporates patients' behavioral and physiological data. G GlucoNet achieves a 60% improvement in RMSE and a 21% reduction in the number of parameters, using data obtained involving 12 participants with T1-Diabetes.
  arXiv  Detail & Related papers  (2024-11-16T05:09:20Z)
• From Glucose Patterns to Health Outcomes: A Generalizable Foundation Model for Continuous Glucose Monitor Data Analysis [50.80532910808962]
  We present GluFormer, a generative foundation model on biomedical temporal data based on a transformer architecture. GluFormer generalizes to 15 different external datasets, including 4936 individuals across 5 different geographical regions. It can also predict onset of future health outcomes even 4 years in advance.
  arXiv  Detail & Related papers  (2024-08-20T13:19:06Z)
• Machine Learning for ALSFRS-R Score Prediction: Making Sense of the Sensor Data [44.99833362998488]
  Amyotrophic Lateral Sclerosis (ALS) is a rapidly progressive neurodegenerative disease that presents individuals with limited treatment options. The present investigation, spearheaded by the iDPP@CLEF 2024 challenge, focuses on utilizing sensor-derived data obtained through an app.
  arXiv  Detail & Related papers  (2024-07-10T19:17:23Z)
• GARNN: An Interpretable Graph Attentive Recurrent Neural Network for Predicting Blood Glucose Levels via Multivariate Time Series [12.618792803757714]
  We propose interpretable graph attentive neural networks (GARNNs) to model multi-modal data. GARNNs achieve the best prediction accuracy and provide high-quality temporal interpretability. These findings underline the potential of GARNN as a robust tool for improving diabetes care.
  arXiv  Detail & Related papers  (2024-02-26T01:18:53Z)
• Improving Diffusion Models for ECG Imputation with an Augmented Template Prior [43.6099225257178]
  noisy and poor-quality recordings are a major issue for signals collected using mobile health systems. Recent studies have explored the imputation of missing values in ECG with probabilistic time-series models. We present a template-guided denoising diffusion probabilistic model (DDPM), PulseDiff, which is conditioned on an informative prior for a range of health conditions.
  arXiv  Detail & Related papers  (2023-10-24T11:34:15Z)
• Machine Learning-Based Diabetes Detection Using Photoplethysmography Signal Features [0.0]
  Diabetes is a prevalent chronic condition that compromises the health of millions of people worldwide. Here, we present an alternative method to overcome shortcomings based on non-invasive optical photoplethysmography for detecting diabetes. We classify non-Diabetic and Diabetic patients using the PPG signal and algorithms for training Logistic Regression and eXtreme Gradient Boosting. Our findings are within the same range reported in the literature, indicating that machine learning methods are promising for developing remote, non-invasive, and continuous measurement devices for detecting and preventing diabetes.
  arXiv  Detail & Related papers  (2023-08-02T14:10:03Z)
• Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction [0.0]
  Glucose prediction is vital to avoid dangerous post-meal complications in treating individuals with diabetes. Traditional methods, such as artificial neural networks, have shown high accuracy rates. We propose a novel glucose prediction method emphasising interpretability.
  arXiv  Detail & Related papers  (2023-07-03T12:22:04Z)
• Machine Learning based prediction of Glucose Levels in Type 1 Diabetes Patients with the use of Continuous Glucose Monitoring Data [0.0]
  Continuous Glucose Monitoring (CGM) devices offer detailed, non-intrusive and real time insights into a patient's blood glucose concentrations. Leveraging advanced Machine Learning (ML) Models as methods of prediction of future glucose levels, gives rise to substantial quality of life improvements.
  arXiv  Detail & Related papers  (2023-02-24T19:10:40Z)
• Learning to diagnose cirrhosis from radiological and histological labels with joint self and weakly-supervised pretraining strategies [62.840338941861134]
  We propose to leverage transfer learning from large datasets annotated by radiologists, to predict the histological score available on a small annex dataset. We compare different pretraining methods, namely weakly-supervised and self-supervised ones, to improve the prediction of the cirrhosis. This method outperforms the baseline classification of the METAVIR score, reaching an AUC of 0.84 and a balanced accuracy of 0.75.
  arXiv  Detail & Related papers  (2023-02-16T17:06:23Z)
• Segmentation of the Myocardium on Late-Gadolinium Enhanced MRI based on 2.5 D Residual Squeeze and Excitation Deep Learning Model [55.09533240649176]
  The aim of this work is to develop an accurate automatic segmentation method based on deep learning models for the myocardial borders on LGE-MRI. A total number of 320 exams (with a mean number of 6 slices per exam) were used for training and 28 exams used for testing. The performance analysis of the proposed ensemble model in the basal and middle slices was similar as compared to intra-observer study and slightly lower at apical slices.
  arXiv  Detail & Related papers  (2020-05-27T20:44:38Z)
• Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data [53.01543207478818]
  This study explores the use of Continuous Glucose Monitoring (CGM) data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction.
  arXiv  Detail & Related papers  (2020-02-06T16:39:44Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.