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A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder.

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Title: A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder.
Authors: Mohiuddin, S G1, Brailsford, S C2, James, C J3, Amor, J D4, Blum, J M5, Crowe, J A6, Magill, E H5, Prociow, P A6
Source: Journal of the Operational Research Society. Mar2013, Vol. 64 Issue 3, p372-383. 12p.
Subjects: Bipolar disorder, Systems design, Patient monitoring, Automatic control systems, Mathematical models, Prototypes
Abstract: This 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. [ABSTRACT FROM AUTHOR]
Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 85406814
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder.
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  Data: <searchLink fieldCode="AR" term="%22Mohiuddin%2C+S+G%22">Mohiuddin, S G</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Brailsford%2C+S+C%22">Brailsford, S C</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22James%2C+C+J%22">James, C J</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Amor%2C+J+D%22">Amor, J D</searchLink><relatesTo>4</relatesTo><br /><searchLink fieldCode="AR" term="%22Blum%2C+J+M%22">Blum, J M</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Crowe%2C+J+A%22">Crowe, J A</searchLink><relatesTo>6</relatesTo><br /><searchLink fieldCode="AR" term="%22Magill%2C+E+H%22">Magill, E H</searchLink><relatesTo>5</relatesTo><br /><searchLink fieldCode="AR" term="%22Prociow%2C+P+A%22">Prociow, P A</searchLink><relatesTo>6</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+Operational+Research+Society%22">Journal of the Operational Research Society</searchLink>. Mar2013, Vol. 64 Issue 3, p372-383. 12p.
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  Data: <searchLink fieldCode="DE" term="%22Bipolar+disorder%22">Bipolar disorder</searchLink><br /><searchLink fieldCode="DE" term="%22Systems+design%22">Systems design</searchLink><br /><searchLink fieldCode="DE" term="%22Patient+monitoring%22">Patient monitoring</searchLink><br /><searchLink fieldCode="DE" term="%22Automatic+control+systems%22">Automatic control systems</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+models%22">Mathematical models</searchLink><br /><searchLink fieldCode="DE" term="%22Prototypes%22">Prototypes</searchLink>
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  Data: This 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. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Journal of the Operational Research Society is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1057/jors.2012.57
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      – Code: eng
        Text: English
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        PageCount: 12
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      – SubjectFull: Bipolar disorder
        Type: general
      – SubjectFull: Systems design
        Type: general
      – SubjectFull: Patient monitoring
        Type: general
      – SubjectFull: Automatic control systems
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      – SubjectFull: Mathematical models
        Type: general
      – SubjectFull: Prototypes
        Type: general
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      – TitleFull: A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder.
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            NameFull: Mohiuddin, S G
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              Text: Mar2013
              Type: published
              Y: 2013
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              Value: 64
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