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Title: Mathematical modelling, forecasting and telemonitoring of mood in bipolar disorder
Author: Moore, Paul J.
Awarding Body: University of Oxford
Current Institution: University of Oxford
Date of Award: 2014
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This study applies statistical models to mood in patients with bipolar disorder. Three analyses of telemonitored mood data are reported, each corresponding to a journal paper by the author. The first analysis reveals that patients whose sleep varies in quality tend to return mood ratings more sporadically than those with less variable sleep quality. The second analysis finds that forecasting depression with weekly data is not feasible using weekly mood ratings. A third analysis shows that depression time series cannot be distinguished from their linear surrogates, and that nonlinear forecasting methods are no more accurate than linear methods in forecasting mood. An additional contribution is the development of a new k-nearest neighbour forecasting algorithm which is evaluated on the mood data and other time series. Further work is proposed on more frequently sampled data and on system identification. Finally, it is suggested that observational data should be combined with models of brain function, and that more work is needed on theoretical explanations for mental illnesses.
Supervisor: Howell, Peter; Little, Max Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available
Keywords: Mathematics ; bipolar disorder ; mood variability ; time series analysis ; public healthcare ; psychiatry