|Appears in Collections:||Computing Science and Mathematics Journal Articles|
|Peer Review Status:||Refereed|
|Title:||A multi-state model to improve the design of an automated system to monitor the activity patterns of patients with bipolar disorder|
|Author(s):||Mohiuddin, Syed Golam|
Brailsford, Sally C
James, Christopher J
Amor, James D
Blum, Jesse Michael
Crowe, John A
Prociow, Pawel A
personalised ambient monitoring
Monte Carlo simulation
|Citation:||Mohiuddin 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.|
|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.|
|Rights:||The 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.|
|jors201257_AOP.pdf||357.88 kB||Adobe PDF||Under Permanent Embargo Request a copy|
Note: If any of the files in this item are currently embargoed, you can request a copy directly from the author by clicking the padlock icon above. However, this facility is dependent on the depositor still being contactable at their original email address.
This item is protected by original copyright
Items in the Repository are protected by copyright, with all rights reserved, unless otherwise indicated.
If you believe that any material held in STORRE infringes copyright, please contact email@example.com providing details and we will remove the Work from public display in STORRE and investigate your claim.