Personalised mobile services supporting the implementation of clinical guidelines

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dc.contributor.author Jones, Val en_US
dc.contributor.author Gay, Valerie en_US
dc.contributor.author Leijdekkers, Peter en_US
dc.contributor.author Rienks, Rienk en_US
dc.contributor.author Hermens, Hermie en_US
dc.contributor.editor F. Grasso and C. Paris en_US
dc.date.accessioned 2010-06-16T05:01:05Z
dc.date.available 2010-06-16T05:01:05Z
dc.date.issued 2009 en_US
dc.identifier 2008005479 en_US
dc.identifier.citation Jones Val et al. 2009, 'Personalised mobile services supporting the implementation of clinical guidelines', AIME 2009, Verona, pp. 10-14. en_US
dc.identifier.issn - en_US
dc.identifier.other E1 en_US
dc.identifier.uri http://hdl.handle.net/10453/12031
dc.description.abstract Telemonitoring is emerging as a compelling application of Body Area Networks (BANs). We describe two health BAN systems developed respectively by a European team and an Australian team and discuss some issues encountered relating to formalization of clinical knowledge to support realtime analysis and interpretation of BAN data. Our example application is an evidence-based telemonitoring and teletreatment application for home-based rehabilitation. The application is intended to support implementation of a clinical guideline for cardiac rehabilitation following myocardial infarction. In addition to this the proposal is to establish the patienta??s individual baseline risk profile and, by real-time analysis of BAN data, continually re-assess the current risk level in order to give timely personalised feedback. Static and dynamic risk factors are derived from literature. Many sources express evidence probabilistically, suggesting a requirement for reasoning with uncertainty; elsewhere evidence requires qualitatie reasoning: both familiar modes of reasoning in KBSs. However even at this knowledge acquisition stage some issues arise concerning how best to apply the clinical evidence. Furthermore, in cases where insufficient clinical evidence is currently available, telemonitoring can yield large collections of clinical data with the potential for data mining in order to furnish more statistically powerful and accurate clinical evidence. en_US
dc.language English en_US
dc.publisher AIME 2009 en_US
dc.relation.hasversion Accepted manuscript version
dc.relation.isbasedon http://www.csc.liv.ac.uk/~floriana/Pers4eHealth09/Pers4eHealth09.pdf en_US
dc.title Personalised mobile services supporting the implementation of clinical guidelines en_US
dc.parent 4th workshop on Personalisation for e-Health en_US
dc.journal.volume en_US
dc.journal.number en_US
dc.publocation Verona en_US
dc.identifier.startpage 10 en_US
dc.identifier.endpage 14 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080600 en_US
dc.personcode 0000050566 en_US
dc.personcode 990935 en_US
dc.personcode 040221 en_US
dc.personcode 0000050567 en_US
dc.personcode 0000050568 en_US
dc.percentage 100 en_US
dc.classification.name Computer Software en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom Workshop on Personalisation for e-Health en_US
dc.date.activity 20090619 en_US
dc.location.activity Verona, Italy en_US
dc.description.keywords Telemonitoring/treatment, Body Area Networks, personalised feedback, biosignal processing, clinical guidelines en_US
dc.staffid en_US


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