Predicting Feedback Compliance in a Teletreatment Application

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    Abstract

    Health care provision is facing resourcing challenges which will further increase in the 21st century. Health care mediated by technology is widely seen as one important element in the struggle to maintain existing standards of care. Personal health monitoring and treatment systems with a high degree of autonomic operation will be required to support self-care. Such systems must provide many services and in most cases must incorporate feedback to patients to advise them how to manage the daily details of their treatment and lifestyle changes. As in many other areas of healthcare, patient compliance is however an issue. In this experiment we apply machine learning techniques to three corpora containing data from trials of body worn systems for activity monitoring and feedback. The overall objective is to investigate how to improve feedback compliance in patients using personal monitoring and treatment systems, by taking into account various contextual features associated with the feedback instances. In this article we describe our first machine learning experiments. The goal of the experiments is twofold: to determine a suitable classification algorithm and to find an optimal set of contextual features to improve the performance of the classifier. The optimal feature set was constructed using genetic algorithms. We report initial results which demonstrate the viability of this approach.
    Original languageUndefined
    Title of host publication3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
    PublisherIEEE
    Pages1-5
    Number of pages5
    ISBN (Print)978-1-4244-8131-6
    DOIs
    Publication statusPublished - 2010
    Event3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010 - Rome, Italy
    Duration: 7 Nov 201010 Nov 2010
    Conference number: 3

    Publication series

    Name
    PublisherIEEE

    Conference

    Conference3rd International Symposium on Applied Sciences in Biomedical and Communication Technologies, ISABEL 2010
    Abbreviated titleISABEL
    CountryItaly
    CityRome
    Period7/11/1010/11/10

    Keywords

    • IR-75931
    • METIS-275920
    • Genetic Algorithms
    • EWI-19553
    • Machine Learning
    • Activity Monitoring
    • feedback compliance
    • Mobile healthcare

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