Nothing Special   »   [go: up one dir, main page]

Bhimireddy et al., 2020 - Google Patents

Blood glucose level prediction as time-series modeling using sequence-to-sequence neural networks

Bhimireddy et al., 2020

View PDF
Document ID
11761343043503873460
Author
Bhimireddy A
Sinha P
Oluwalade B
Gichoya J
Purkayastha S
Publication year

External Links

Snippet

The management of blood glucose levels is critical in the care of Type 1 diabetes subjects. In extremes, high or low levels of blood glucose are fatal. To avoid such adverse events, there is the development and adoption of wearable technologies that continuously monitor …
Continue reading at scholarworks.iupui.edu (PDF) (other versions)

Classifications

    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/345Medical expert systems, neural networks or other automated diagnosis
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3437Medical simulation or modelling, e.g. simulating the evolution of medical disorders
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/32Medical data management, e.g. systems or protocols for archival or communication of medical images, computerised patient records or computerised general medical references
    • G06F19/322Management of patient personal data, e.g. patient records, conversion of records or privacy aspects
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/30Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
    • G06F19/34Computer-assisted medical diagnosis or treatment, e.g. computerised prescription or delivery of medication or diets, computerised local control of medical devices, medical expert systems or telemedicine
    • G06F19/3456Computer-assisted prescription or delivery of medication, e.g. prescription filling or compliance checking
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F17/00Digital computing or data processing equipment or methods, specially adapted for specific functions
    • G06F17/30Information retrieval; Database structures therefor; File system structures therefor
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F19/00Digital computing or data processing equipment or methods, specially adapted for specific applications
    • G06F19/10Bioinformatics, i.e. methods or systems for genetic or protein-related data processing in computational molecular biology
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F11/00Error detection; Error correction; Monitoring
    • G06F11/07Error detection; Error correction; Monitoring responding to the occurence of a fault, e.g. fault tolerance
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06QDATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00Systems or methods specially adapted for a specific business sector, e.g. utilities or tourism
    • G06Q50/10Services
    • G06Q50/22Health care, e.g. hospitals; Social work
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06NCOMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00Computer systems based on biological models
    • G06N3/02Computer systems based on biological models using neural network models
    • GPHYSICS
    • G06COMPUTING; CALCULATING; COUNTING
    • G06FELECTRICAL DIGITAL DATA PROCESSING
    • G06F3/00Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
    • G06F3/01Input arrangements or combined input and output arrangements for interaction between user and computer
    • AHUMAN NECESSITIES
    • A61MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61BDIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00Detecting, measuring or recording for diagnostic purposes; Identification of persons
    • A61B5/145Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue
    • A61B5/14532Measuring characteristics of blood in vivo, e.g. gas concentration, pH value; Measuring characteristics of body fluids or tissues, e.g. interstitial fluid, cerebral tissue for measuring glucose, e.g. by tissue impedance measurement

Similar Documents

Publication Publication Date Title
Bhimireddy et al. Blood glucose level prediction as time-series modeling using sequence-to-sequence neural networks
Martinsson et al. Blood glucose prediction with variance estimation using recurrent neural networks
Pappada et al. Neural network-based real-time prediction of glucose in patients with insulin-dependent diabetes
Zhu et al. Enhancing self-management in type 1 diabetes with wearables and deep learning
Afsaneh et al. Recent applications of machine learning and deep learning models in the prediction, diagnosis, and management of diabetes: a comprehensive review
Zaitcev et al. A deep neural network application for improved prediction of $\text {HbA} _ {\text {1c}} $ in type 1 diabetes
Kumari et al. A data mining approach for the diagnosis of diabetes mellitus
De Bois et al. GLYFE: review and benchmark of personalized glucose predictive models in type 1 diabetes
EP3731753A1 (en) Systems and methods for prediction of glycemia and decisions support
Seo et al. A personalized blood glucose level prediction model with a fine-tuning strategy: A proof-of-concept study
Sheikhalishahi et al. Benchmarking machine learning models on eICU critical care dataset
Cui et al. Personalised short-term glucose prediction via recurrent self-attention network
Zaidi et al. Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients
Padmapritha Prediction of Blood Glucose Level by using an LSTM based Recurrent Neural networks
Wang et al. Blood glucose forecasting using lstm variants under the context of open source artificial pancreas system
Parra et al. Learning Difference Equations with Structured Grammatical Evolution for Postprandial Glycaemia Prediction
Annuzzi et al. Assessing the Features on Blood Glucose Level Prediction in Type 1 Diabetes Patients Through Explainable Artificial Intelligence
Han et al. Glu-Ensemble: An ensemble deep learning framework for blood glucose forecasting in type 2 diabetes patients
Lutsker et al. From glucose patterns to health outcomes: A generalizable foundation model for continuous glucose monitor data analysis
Al-Dailami et al. Attention-based memory fusion network for clinical outcome prediction using electronic medical records
CN113782209A (en) Intelligent chronic patient prognosis method and system based on recurrent neural network
Butunoi et al. Short-term glucose prediction in Type 1 Diabetes
Shi et al. Deep Learning Preserving Renal Dialysis Treatment Recommendation
Domanski et al. Advancing blood glucose prediction with neural architecture search and deep reinforcement learning for type 1 diabetics
Fitzgerald et al. Continuous time recurrent neural networks: overview and application to forecasting blood glucose in the intensive care unit