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 Golamen_UK
dc.contributor.authorBrailsford, Sally Cen_UK
dc.contributor.authorJames, Christopher Jen_UK
dc.contributor.authorAmor, James Den_UK
dc.contributor.authorBlum, Jesse Michaelen_UK
dc.contributor.authorCrowe, John Aen_UK
dc.contributor.authorMagill, Evanen_UK
dc.contributor.authorProciow, Pawel Aen_UK
dc.date.accessioned2013-03-20T15:57:06Z-
dc.date.available2013-03-20T15:57:06Zen_UK
dc.date.issued2013-03en_UK
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.isoenen_UK
dc.publisherPalgrave MacMillanen_UK
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. https://doi.org/10.1057/jors.2012.57en_UK
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.en_UK
dc.rights.urihttp://www.rioxx.net/licenses/under-embargo-all-rights-reserveden_UK
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.embargodate2999-12-10en_UK
dc.rights.embargoreason[jors201257_AOP.pdf] The 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.en_UK
dc.identifier.doi10.1057/jors.2012.57en_UK
dc.citation.jtitleJournal of the Operational Research Societyen_UK
dc.citation.issn1476-9360en_UK
dc.citation.issn0160-5682en_UK
dc.citation.volume64en_UK
dc.citation.issue3en_UK
dc.citation.spage372en_UK
dc.citation.epage383en_UK
dc.citation.publicationstatusPublisheden_UK
dc.citation.peerreviewedRefereeden_UK
dc.type.statusVoR - Version of Recorden_UK
dc.contributor.funderEngineering and Physical Sciences Research Councilen_UK
dc.author.emailehm@cs.stir.ac.uken_UK
dc.citation.date09/05/2012en_UK
dc.contributor.affiliationUniversity of Manchesteren_UK
dc.contributor.affiliationUniversity of Southamptonen_UK
dc.contributor.affiliationUniversity of Warwicken_UK
dc.contributor.affiliationUniversity of Southamptonen_UK
dc.contributor.affiliationComputing Science and Mathematics - Divisionen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.contributor.affiliationComputing Scienceen_UK
dc.contributor.affiliationUniversity of Nottinghamen_UK
dc.identifier.isiWOS:000314921600006en_UK
dc.identifier.scopusid2-s2.0-84873926258en_UK
dc.identifier.wtid754543en_UK
dc.contributor.orcid0000-0003-0505-406Xen_UK
dc.date.accepted2012-03-01en_UK
dcterms.dateAccepted2012-03-01en_UK
dc.date.filedepositdate2012-10-19en_UK
dc.relation.funderprojectEnabling health, independence and wellbeing for Psychiatric patients through personalised ambient Monitoring (PAM)en_UK
dc.relation.funderrefEP/F003684/1en_UK
rioxxterms.typeJournal Article/Reviewen_UK
rioxxterms.versionVoRen_UK
local.rioxx.authorMohiuddin, Syed Golam|en_UK
local.rioxx.authorBrailsford, Sally C|en_UK
local.rioxx.authorJames, Christopher J|en_UK
local.rioxx.authorAmor, James D|en_UK
local.rioxx.authorBlum, Jesse Michael|en_UK
local.rioxx.authorCrowe, John A|en_UK
local.rioxx.authorMagill, Evan|0000-0003-0505-406Xen_UK
local.rioxx.authorProciow, Pawel A|en_UK
local.rioxx.projectEP/F003684/1|Engineering and Physical Sciences Research Council|http://dx.doi.org/10.13039/501100000266en_UK
local.rioxx.freetoreaddate2999-12-10en_UK
local.rioxx.licencehttp://www.rioxx.net/licenses/under-embargo-all-rights-reserved||en_UK
local.rioxx.filenamejors201257_AOP.pdfen_UK
local.rioxx.filecount1en_UK
local.rioxx.source0160-5682en_UK
Appears in Collections:Computing Science and Mathematics Journal Articles

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