Abstract
Bipolar disorder is a mental illness affecting over 1% of the world’s population. In the course of disease there are episodic fluctuations between different mood phases, ranging from depression to manic episodes and mixed states. Early detection and treatment of prodromal symptoms of affective episode recurrence is crucial since it reduces the conversion rates to full-blown illness and decreases the symptoms severity. This can be achieved by monitoring the mood stability with the use of data collected from patients’ smartphones. We provide an illustrative example of the application of control charts to early and reliably generate notifications about the change of the bipolarity phase. Our charts are designed with the weighted model averaging approaches WAM* and WAMs for the detection of disturbances in the stability of the monitored processes. The models are selected in a novel way using the autocorrelation functions. The proposed approach delivers results that have clear, psychiatric interpretation. Control charts based on weighted model averaging are a promising tool for monitoring patients suffering from bipolar disorder, especially in case of limited amount of diagnostic data.
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Acknowledgments
The study was submitted to the Office for Registration of Medicinal Products, Medical Devices and Biocidal Products in accordance with Polish law. This project is financed from EU funds (Regional Operational Program for Mazovia) - a project entitled “Smartphone-based diagnostics of phase changes in the course of bipolar disorder” (RPMA.01.02.00-14-5706/16-00).
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Kaczmarek-Majer, K. et al. (2019). Control Charts Designed Using Model Averaging Approach for Phase Change Detection in Bipolar Disorder. In: Destercke, S., Denoeux, T., Gil, M., Grzegorzewski, P., Hryniewicz, O. (eds) Uncertainty Modelling in Data Science. SMPS 2018. Advances in Intelligent Systems and Computing, vol 832. Springer, Cham. https://doi.org/10.1007/978-3-319-97547-4_16
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DOI: https://doi.org/10.1007/978-3-319-97547-4_16
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