An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients

• URL: http://arxiv.org/abs/2305.10353v1
• Date: Thu, 11 May 2023 07:28:40 GMT
• Title: An Ensemble Learning Approach for Exercise Detection in Type 1 Diabetes Patients
• Authors: Ke Ma, Hongkai Chen, Shan Lin
• Abstract summary: We propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.
• Score: 9.491537214222756
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Type 1 diabetes is a serious disease in which individuals are unable to regulate their blood glucose levels, leading to various medical complications. Artificial pancreas (AP) systems have been developed as a solution for type 1 diabetic patients to mimic the behavior of the pancreas and regulate blood glucose levels. However, current AP systems lack detection capabilities for exercise-induced glucose intake, which can last up to 4 to 8 hours. This incapability can lead to hypoglycemia, which if left untreated, could have serious consequences, including death. Existing exercise detection methods are either limited to single sensor data or use inaccurate models for exercise detection, making them less effective in practice. In this work, we propose an ensemble learning framework that combines a data-driven physiological model and a Siamese network to leverage multiple physiological signal streams for exercise detection with high accuracy. To evaluate the effectiveness of our proposed approach, we utilized a public dataset with 12 diabetic patients collected from an 8-week clinical trial. Our approach achieves a true positive rate for exercise detection of 86.4% and a true negative rate of 99.1%, outperforming state-of-the-art solutions.

Related papers

• Chronic Disease Diagnoses Using Behavioral Data [42.96592744768303]
  We aim to diagnose hyperglycemia (diabetes), hyperlipidemia, and hypertension (collectively known as 3H) using own collected behavioral data.
  arXiv  Detail & Related papers  (2024-10-04T12:52:49Z)
• 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)
• Hearing Your Blood Sugar: Non-Invasive Glucose Measurement Through Simple Vocal Signals, Transforming any Speech into a Sensor with Machine Learning [0.0]
  We present a transformative and straightforward method that utilizes voice analysis to predict blood glucose levels. By applying advanced machine learning algorithms, we analyzed vocal signal variations and established a significant correlation with blood glucose levels. Our findings indicate that voice analysis may serve as a viable non-invasive alternative for glucose monitoring.
  arXiv  Detail & Related papers  (2024-08-15T12:13:23Z)
• Attention Networks for Personalized Mealtime Insulin Dosing in People with Type 1 Diabetes [0.30723404270319693]
  We demonstrate how a reinforcement learning agent, employing a self-attention encoder network, can effectively mimic and enhance this intuitive process. Results reveal a significant reduction in glycemic risk, from 16.5 to 9.6 in scenarios using sensor-augmented pump and from 9.1 to 6.7 in scenarios using automated insulin delivery.
  arXiv  Detail & Related papers  (2024-06-18T17:59:32Z)
• 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)
• Prognosis and Treatment Prediction of Type-2 Diabetes Using Deep Neural Network and Machine Learning Classifiers [1.1470070927586016]
  The motion of this research is a comparative study of seven machine learning classifiers and an artificial neural network method to prognosticate the detection and treatment of diabetes with high accuracy. Our training and test dataset is an accumulation of 9483 diabetes patients information. Our high-performing model can be used by hospitals to predict diabetes and drive research into more accurate prediction models.
  arXiv  Detail & Related papers  (2023-01-08T19:10:20Z)
• Learning shape distributions from large databases of healthy organs: applications to zero-shot and few-shot abnormal pancreas detection [56.4729555813972]
  We propose a scalable and data-driven approach to learn shape distributions from large databases of healthy organs. The resulting latent shape representations are leveraged to derive zeroshot and few-shot methods for abnormal shape detection.
  arXiv  Detail & Related papers  (2022-10-21T16:39:59Z)
• Identification of Ischemic Heart Disease by using machine learning technique based on parameters measuring Heart Rate Variability [50.591267188664666]
  In this study, 18 non-invasive features (age, gender, left ventricular ejection fraction and 15 obtained from HRV) of 243 subjects were used to train and validate a series of several ANN. The best result was obtained using 7 input parameters and 7 hidden nodes with an accuracy of 98.9% and 82% for the training and validation dataset.
  arXiv  Detail & Related papers  (2020-10-29T19:14:41Z)
• Personalized pathology test for Cardio-vascular disease: Approximate Bayesian computation with discriminative summary statistics learning [48.7576911714538]
  We propose a platelet deposition model and an inferential scheme to estimate the biologically meaningful parameters using approximate computation. This work opens up an unprecedented opportunity of personalized pathology test for CVD detection and medical treatment.
  arXiv  Detail & Related papers  (2020-10-13T15:20:21Z)
• Basal Glucose Control in Type 1 Diabetes using Deep Reinforcement Learning: An In Silico Validation [16.93692520921499]
  We propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In the adult cohort, percentage time in target range improved from 77.6% to 80.9% with single-hormone control. In the adolescent cohort, percentage time in target range improved from 55.5% to 65.9% with single-hormone control.
  arXiv  Detail & Related papers  (2020-05-18T20:13:16Z)
• Automated Diabetic Retinopathy Grading using Deep Convolutional Neural Network [0.7646713951724012]
  The competence of computer-aided detection systems to accurately detect the Diabetic Retinopathy had popularized them among researchers. In this study, we have utilized a pre-trained DenseNet121 network with several modifications and trained on APTOS 2019 dataset. The proposed method outperformed other state-of-the-art networks in early-stage detection and achieved 96.51% accuracy in severity grading of Diabetic Retinopathy for multi-label classification.
  arXiv  Detail & Related papers  (2020-04-14T07:37:21Z)

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.