Abstract
Bipolar disorder (BD) is a serious mental disorder characterized by manic episodes of elevated mood and overactivity, interspersed with periods of depression. Typically, the psychiatric assessment of affective state is carried out by a psychiatrist during routine check-up visits. However, diagnostics of a phase change can be facilitated by monitoring data collected by the patient’s smartphone. Previous studies concentrated primarily on the phase detection formulated as a classification task. In this study, we introduce a new approach to predict the phase change of BD patients using acoustic features and a combination of the Kohonen’s self-organizing maps and random forests. The primary goal is to predict the forthcoming change of patient’s state. We report on preliminary results that confirm the existence of a relation between the outcome of unsupervised learning (clustering) and the psychiatric assessment. Next, we evaluate the out-of-sample accuracy to predict the patient’s state with random forests. Finally, we discuss the potential of unsupervised learning for monitoring BD patients.
This work was partially 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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Acknowledgments
The study was submitted to the Office for Registration of Medicinal Products, Medical Devices and Biocidal Products in accordance with Polish law. This work was partially 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). The authors thank psychiatrists that participated in the observational trial for their commitment and advice. The authors thank the researchers Weronika Radziszewska and Anna Olwert from Systems Research Institute, Polish Academy of Sciences for their support in data preparation and analysis, as well as Małgorzata Igras-Cybulska and Bartosz Ziółko from Techmo sp. z o.o. for their support in the extraction of acoustic features.
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The study obtained the consent of the Bioethical Commission at the District Medical Chamber in Warsaw (agreement no. KB/1094/17).
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Kamińska, O. et al. (2019). Self-organizing Maps Using Acoustic Features for Prediction of State Change in Bipolar Disorder. In: Marcos, M., et al. Artificial Intelligence in Medicine: Knowledge Representation and Transparent and Explainable Systems. KR4HC TEAAM 2019 2019. Lecture Notes in Computer Science(), vol 11979. Springer, Cham. https://doi.org/10.1007/978-3-030-37446-4_12
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DOI: https://doi.org/10.1007/978-3-030-37446-4_12
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