Literature DB >> 24407050

glUCModel: a monitoring and modeling system for chronic diseases applied to diabetes.

J Ignacio Hidalgo1, Esther Maqueda2, José L Risco-Martín3, Alfredo Cuesta-Infante4, J Manuel Colmenar4, Javier Nobel1.   

Abstract

Chronic patients must carry out a rigorous control of diverse factors in their lives. Diet, sport activity, medical analysis or blood glucose levels are some of them. This is a hard task, because some of these controls are performed very often, for instance some diabetics measure their glucose levels several times every day, or patients with chronic renal disease, a progressive loss in renal function, should strictly control their blood pressure and diet. In order to facilitate this task to both the patient and the physician, we have developed a web application for chronic diseases control which we have particularized to diabetes. This system, called glUCModel, improves the communication and interaction between patients and doctors, and eventually the quality of life of the former. Through a web application, patients can upload their personal and medical data, which are stored in a centralized database. In this way, doctors can consult this information and have a better control over patient records. glUCModel also presents three novelties in the disease management: a recommender system, an e-learning course and a module for automatic generation of glucose levels model. The recommender system uses Case Based Reasoning. It provides automatic recommendations to the patient, based on the recorded data and physician preferences, to improve their habits and knowledge about the disease. The e-learning course provides patients a space to consult information about the illness, and also to assess their own knowledge about the disease. Blood glucose levels are modeled by means of evolutionary computation, allowing to predict glucose levels using particular features of each patient. glUCModel was developed as a system where a web layer allows the access of the users from any device connected to the Internet, like desktop computers, tablets or mobile phones.
Copyright © 2014 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Diabetes; Internet; Self-management; Telemedicine; Web applications

Mesh:

Substances:

Year:  2014        PMID: 24407050     DOI: 10.1016/j.jbi.2013.12.015

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  7 in total

1.  Artificial Intelligence Methodologies and Their Application to Diabetes.

Authors:  Mercedes Rigla; Gema García-Sáez; Belén Pons; Maria Elena Hernando
Journal:  J Diabetes Sci Technol       Date:  2017-05-25

2.  Tailoring motivational health messages for smoking cessation using an mHealth recommender system integrated with an electronic health record: a study protocol.

Authors:  Santiago Hors-Fraile; Francine Schneider; Luis Fernandez-Luque; Francisco Luna-Perejon; Anton Civit; Dimitris Spachos; Panagiotis Bamidis; Hein de Vries
Journal:  BMC Public Health       Date:  2018-06-05       Impact factor: 3.295

Review 3.  Artificial Intelligence for Diabetes Management and Decision Support: Literature Review.

Authors:  Ivan Contreras; Josep Vehi
Journal:  J Med Internet Res       Date:  2018-05-30       Impact factor: 5.428

4.  Patient-Generated Health Data Integration and Advanced Analytics for Diabetes Management: The AID-GM Platform.

Authors:  Elisa Salvi; Pietro Bosoni; Valentina Tibollo; Lisanne Kruijver; Valeria Calcaterra; Lucia Sacchi; Riccardo Bellazzi; Cristiana Larizza
Journal:  Sensors (Basel)       Date:  2019-12-24       Impact factor: 3.576

Review 5.  Patient-Generated Data Analytics of Health Behaviors of People Living With Type 2 Diabetes: Scoping Review.

Authors:  Meghan S Nagpal; Antonia Barbaric; Diana Sherifali; Plinio P Morita; Joseph A Cafazzo
Journal:  JMIR Diabetes       Date:  2021-12-20

Review 6.  Health Recommender Systems: Systematic Review.

Authors:  Robin De Croon; Leen Van Houdt; Nyi Nyi Htun; Gregor Štiglic; Vero Vanden Abeele; Katrien Verbert
Journal:  J Med Internet Res       Date:  2021-06-29       Impact factor: 5.428

Review 7.  How recommender systems could support and enhance computer-tailored digital health programs: A scoping review.

Authors:  Kei Long Cheung; Dilara Durusu; Xincheng Sui; Hein de Vries
Journal:  Digit Health       Date:  2019-01-24
  7 in total

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