Literature DB >> 20123578

SMARTDIAB: a communication and information technology approach for the intelligent monitoring, management and follow-up of type 1 diabetes patients.

Stavroula G Mougiakakou1, Christos S Bartsocas, Evangelos Bozas, Nikos Chaniotakis, Dimitra Iliopoulou, Ioannis Kouris, Sotiris Pavlopoulos, Aikaterini Prountzou, Marios Skevofilakas, Alexandre Tsoukalis, Kostas Varotsis, Andrianni Vazeou, Konstantia Zarkogianni, Konstantina S Nikita.   

Abstract

SMARTDIAB is a platform designed to support the monitoring, management, and treatment of patients with type 1 diabetes mellitus (T1DM), by combining state-of-the-art approaches in the fields of database (DB) technologies, communications, simulation algorithms, and data mining. SMARTDIAB consists mainly of two units: 1) the patient unit (PU); and 2) the patient management unit (PMU), which communicate with each other for data exchange. The PMU can be accessed by the PU through the internet using devices, such as PCs/laptops with direct internet access or mobile phones via a Wi-Fi/General Packet Radio Service access network. The PU consists of an insulin pump for subcutaneous insulin infusion to the patient and a continuous glucose measurement system. The aforementioned devices running a user-friendly application gather patient's related information and transmit it to the PMU. The PMU consists of a diabetes data management system (DDMS), a decision support system (DSS) that provides risk assessment for long-term diabetes complications, and an insulin infusion advisory system (IIAS), which reside on a Web server. The DDMS can be accessed from both medical personnel and patients, with appropriate security access rights and front-end interfaces. The DDMS, apart from being used for data storage/retrieval, provides also advanced tools for the intelligent processing of the patient's data, supporting the physician in decision making, regarding the patient's treatment. The IIAS is used to close the loop between the insulin pump and the continuous glucose monitoring system, by providing the pump with the appropriate insulin infusion rate in order to keep the patient's glucose levels within predefined limits. The pilot version of the SMARTDIAB has already been implemented, while the platform's evaluation in clinical environment is being in progress.

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Year:  2010        PMID: 20123578     DOI: 10.1109/TITB.2009.2039711

Source DB:  PubMed          Journal:  IEEE Trans Inf Technol Biomed        ISSN: 1089-7771


  15 in total

1.  Special issue on emerging technologies for the management of diabetes mellitus.

Authors:  Konstantia Zarkogianni; Konstantina S Nikita
Journal:  Med Biol Eng Comput       Date:  2015-12       Impact factor: 2.602

2.  Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring.

Authors:  K Zarkogianni; K Mitsis; E Litsa; M-T Arredondo; G Ficο; A Fioravanti; K S Nikita
Journal:  Med Biol Eng Comput       Date:  2015-06-07       Impact factor: 2.602

3.  Performance assessment of a closed-loop system for diabetes management.

Authors:  A Martinez-Millana; G Fico; C Fernández-Llatas; V Traver
Journal:  Med Biol Eng Comput       Date:  2015-02-11       Impact factor: 2.602

4.  An information technology framework for strengthening telehealthcare service delivery.

Authors:  Li-Chin Chen; Chi-Wen Chen; Yung-Ching Weng; Rung-Ji Shang; Hui-Chu Yu; Yufang Chung; Feipei Lai
Journal:  Telemed J E Health       Date:  2012-10       Impact factor: 3.536

Review 5.  Smart health monitoring systems: an overview of design and modeling.

Authors:  Mirza Mansoor Baig; Hamid Gholamhosseini
Journal:  J Med Syst       Date:  2013-01-15       Impact factor: 4.460

Review 6.  A Review of Emerging Technologies for the Management of Diabetes Mellitus.

Authors:  Konstantia Zarkogianni; Eleni Litsa; Konstantinos Mitsis; Po-Yen Wu; Chanchala D Kaddi; Chih-Wen Cheng; May D Wang; Konstantina S Nikita
Journal:  IEEE Trans Biomed Eng       Date:  2015-08-19       Impact factor: 4.538

7.  Evaluation of short-term predictors of glucose concentration in type 1 diabetes combining feature ranking with regression models.

Authors:  Eleni I Georga; Vasilios C Protopappas; Demosthenes Polyzos; Dimitrios I Fotiadis
Journal:  Med Biol Eng Comput       Date:  2015-03-15       Impact factor: 2.602

8.  An early warning system for hypoglycemic/hyperglycemic events based on fusion of adaptive prediction models.

Authors:  Elena Daskalaki; Kirsten Nørgaard; Thomas Züger; Aikaterini Prountzou; Peter Diem; Stavroula Mougiakakou
Journal:  J Diabetes Sci Technol       Date:  2013-05-01

Review 9.  Medical software applications for in-hospital insulin therapy: A systematic review.

Authors:  Julia Mandaro Lavinas Jones; Alina Coutinho Rodrigues Feitosa; Malena Costa Hita; Elisabeth Martinez Fonseca; Rodrigo Braga Pato; Marcos Tadashi Kakitani Toyoshima
Journal:  Digit Health       Date:  2020-12-26

10.  A data encryption solution for mobile health apps in cooperation environments.

Authors:  Bruno M Silva; Joel J P C Rodrigues; Fábio Canelo; Ivo C Lopes; Liang Zhou
Journal:  J Med Internet Res       Date:  2013-04-25       Impact factor: 5.428

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