Abstract
Online communities are emerging as important sources of support for people with chronic illnesses such as diabetes and obesity, both of which have been associated with depression. The goal of this study was to assess the performance of text classification in identifying at-risk patients. We manually created a corpus of chat messages based on the ICD-10 depression diagnostic criteria, and trained multiple classifiers on the corpus. After selecting informative features and significant bigrams, a precision of 0.92, recall of 0.88, f-score of 0.92 was reached. Current findings demonstrate the feasibility of automatically identifying patients at risk of developing severe depression in online communities.
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Acknowledgment
This work was supported in part by the Research Program for Telemedicine (HST), Helse Nord RHF, Norway.
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© 2014 Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Chomutare, T. (2014). Text Classification to Automatically Identify Online Patients Vulnerable to Depression. In: Cipresso, P., Matic, A., Lopez, G. (eds) Pervasive Computing Paradigms for Mental Health. MindCare 2014. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 100. Springer, Cham. https://doi.org/10.1007/978-3-319-11564-1_13
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DOI: https://doi.org/10.1007/978-3-319-11564-1_13
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