Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/9740
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dc.contributor.authorMohiuddin, Syed Golam-
dc.contributor.authorBrailsford, Sally C-
dc.contributor.authorJames, Christopher J-
dc.contributor.authorAmor, James D-
dc.contributor.authorBlum, Jesse Michael-
dc.contributor.authorCrowe, John A-
dc.contributor.authorMagill, Evan-
dc.contributor.authorProciow, Pawel A-
dc.date.accessioned2013-03-20T15:57:06Z-
dc.date.issued2013-03-
dc.identifier.urihttp://hdl.handle.net/1893/9740-
dc.description.abstractThis paper describes the role of mathematical modelling in the design and evaluation of an automated system of wearable and environmental sensors called PAM (Personalised Ambient Monitoring) to monitor the activity patterns of patients with bipolar disorder (BD). The modelling work was part of an EPSRC-funded project, also involving biomedical engineers and computer scientists, to develop a prototype PAM system. BD is a chronic, disabling mental illness associated with recurrent severe episodes of mania and depression, interspersed with periods of remission. Early detection of the onset of an acute episode is crucial for effective treatment and control. The aim of PAM is to enable patients with BD to self-manage their condition, by identifying the person's normal ‘activity signature’ and thus automatically detecting tiny changes in behaviour patterns which could herald the possible onset of an acute episode. PAM then alerts the patient to take appropriate action in time to prevent further deterioration and possible hospitalisation. A disease state transition model for BD was developed, using data from the clinical literature, and then used stochastically in a Monte Carlo simulation to test a wide range of monitoring scenarios. The minimum best set of sensors suitable to detect the onset of acute episodes (of both mania and depression) is identified, and the performance of the PAM system evaluated for a range of personalised choices of sensors.en_UK
dc.language.isoen-
dc.publisherPalgrave MacMillan-
dc.relationMohiuddin SG, Brailsford SC, James CJ, Amor JD, Blum JM, Crowe JA, Magill E & Prociow PA (2013) A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder, Journal of the Operational Research Society, 64 (3), pp. 372-383.-
dc.rightsThe publisher does not allow this work to be made publicly available in this Repository. Please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study.-
dc.subjectmental healthen_UK
dc.subjectbipolar disorderen_UK
dc.subjectactivity signaturesen_UK
dc.subjectpersonalised ambient monitoringen_UK
dc.subjectMonte Carlo simulationen_UK
dc.titleA multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorderen_UK
dc.typeJournal Articleen_UK
dc.rights.embargodate2999-12-31T00:00:00Z-
dc.rights.embargoreasonThe publisher does not allow this work to be made publicly available in this Repository therefore there is an embargo on the full text of the work.-
dc.identifier.doihttp://dx.doi.org/10.1057/jors.2012.57-
dc.citation.jtitleJournal of the Operational Research Society-
dc.citation.issn0160-5682-
dc.citation.volume64-
dc.citation.issue3-
dc.citation.spage372-
dc.citation.epage383-
dc.citation.publicationstatusPublished-
dc.citation.peerreviewedRefereed-
dc.type.statusPublisher version (final published refereed version)-
dc.author.emailehm@cs.stir.ac.uk-
dc.citation.date09/05/2012-
dc.contributor.affiliationUniversity of Manchester-
dc.contributor.affiliationUniversity of Southampton-
dc.contributor.affiliationUniversity of Warwick-
dc.contributor.affiliationUniversity of Southampton-
dc.contributor.affiliationComputing Science and Mathematics-
dc.contributor.affiliationUniversity of Nottingham-
dc.contributor.affiliationComputing Science - CSM Dept-
dc.contributor.affiliationUniversity of Nottingham-
dc.rights.embargoterms2999-12-31-
dc.rights.embargoliftdate2999-12-31-
dc.identifier.isi000314921600006-
Appears in Collections:Computing Science and Mathematics Journal Articles

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