Literature DB >> 22256271

Heterogeneous data fusion and intelligent techniques embedded in a mobile application for real-time chronic disease management.

Christos Bellos1, Athanassios Papadopoulos, Roberto Rosso, Dimitrios I Fotiadis.   

Abstract

CHRONIOUS system is an integrated platform aiming at the management of chronic disease patients. One of the most important components of the system is a Decision Support System (DSS) that has been developed in a Smart Device (SD). This component decides on patient's current health status by combining several data, which are acquired either by wearable sensors or manually inputted by the patient or retrieved from the specific database. In case no abnormal situation has been tracked, the DSS takes no action and remains deactivated until next abnormal situation pack of data are being acquired or next scheduled data being transmitted. The DSS that has been implemented is an integrated classification system with two parallel classifiers, combining an expert system (rule-based system) and a supervised classifier, such as Support Vector Machines (SVM), Random Forests, artificial Neural Networks (aNN like the Multi-Layer Perceptron), Decision Trees and Naïve Bayes. The above categorized system is useful for providing critical information about the health status of the patient.

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Mesh:

Year:  2011        PMID: 22256271     DOI: 10.1109/IEMBS.2011.6092047

Source DB:  PubMed          Journal:  Conf Proc IEEE Eng Med Biol Soc        ISSN: 1557-170X


  4 in total

1.  Machine Learning and Mobile Health Monitoring Platforms: A Case Study on Research and Implementation Challenges.

Authors:  Omar Boursalie; Reza Samavi; Thomas E Doyle
Journal:  J Healthc Inform Res       Date:  2018-05-22

2.  Approach for the Development of a Framework for the Identification of Activities of Daily Living Using Sensors in Mobile Devices.

Authors:  Ivan Miguel Pires; Nuno M Garcia; Nuno Pombo; Francisco Flórez-Revuelta; Susanna Spinsante
Journal:  Sensors (Basel)       Date:  2018-02-21       Impact factor: 3.576

3.  Improving Human Activity Recognition Performance by Data Fusion and Feature Engineering.

Authors:  Jingcheng Chen; Yining Sun; Shaoming Sun
Journal:  Sensors (Basel)       Date:  2021-01-20       Impact factor: 3.576

4.  From Data Acquisition to Data Fusion: A Comprehensive Review and a Roadmap for the Identification of Activities of Daily Living Using Mobile Devices.

Authors:  Ivan Miguel Pires; Nuno M Garcia; Nuno Pombo; Francisco Flórez-Revuelta
Journal:  Sensors (Basel)       Date:  2016-02-02       Impact factor: 3.576

  4 in total

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