Abstract
Depression has become a critical illness in human society as many people suffer from the condition without being aware of it. The goal of this paper is to design a system to identify potential depression candidates based on their write-ups. To solve this problem, we propose a two-stage supervised learning framework. The first stage determines whether the user possesses apparent negative emotion. Then the positive cases are passed to the second stage to further evaluate whether the condition is clinical depression or just ordinary sadness. Our training data are generated automatically from Bulletin Board Systems. The content and temporal features are designed to improve the classification accuracy. Finally we develop an online demo system that takes a piece of written text as input, and outputs the likelihood of the author currently suffering depression. We conduct cross-validation and human study to evaluate the effectiveness of this system.
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References
Aamodt, A., Gundersen, O.E., Loge, J.H., Wasteson, E., Szczepanski, T.: Case-Based Reasoning for Assessment and Diagnosis of Depression in Palliative Care. In: The International Symposium on Computer-Based Medical Systems, pp. 480–285 (2010)
American Psychiatric Association, Diagnostic and Statistical Manual of Mental Disorders, 4th edn., Text Revision. American Psychiatric Association, Washington, DC (2000)
Cohn, J.F., Kruez, T.S., Matthews, I., Yang, Y., Nguyen, M.H., Padilla, M.T., Zhou, F., De la Torre, F.: Detecting Depression from Facial Actions and Vocal Prosody. In: International Conference on Affective Computing and Intelligent Interaction (2009)
Eastwood, M.R., Stiasny, S.: Psychiatric Disorder, Hospital Admission, and Season. Archives of General Psychiatry 35, 769–771 (1978)
Fan, R.E., Chang, K.W., Hsieh, C.J., Wang, X.R., Lin, C.J.: LIBLINEAR: A Library for Large Linear Classification. Journal of Machine Learning Research 9, 1871–1874 (2008)
Feightner, J.W., Worrall, G.: Early Detection of Depression by Primary Care Physicians. Can. Med. Assoc. J. 142, 1215–1220 (1990)
Hosseinifard, B., Moradi, M.H., Rostami, R.: Classifying Depression Patients and Normal Subjects Using Machine Learning Techniques. In: Iranian Conference on Electrical Engineering, pp. 1–4 (2011)
Jarrold, W.L., Peintner, B., Yeh, E., Krasnow, R., Javitz, H.S., Swan, G.E.: Language Analytics for Assessing Brain Health: Cognitive Impairment, Depression and Pre-symptomatic Alzheimer’s Disease. Brain Informatics, 299–307 (2010)
Kerkhofs, M., Linkowski, P., Lucas, F., Mendelwicz, J.: Twenty-Four-Hour Patterns of Sleep in Depression. Sleep 14, 501–506 (1991)
Low, L.A., Maddage, N.C., Lech, M., Sheeber, L., Allen, N.: Influence of Acoustic Low-Level Descriptors in the Detection of Clinical Depression in Adolescents. In: ICASSP, pp. 5154–5157 (2010)
Maddage, N.C., Senaratne, R., Low, L.A., Lech, M., Allen, N.: Video-based Detection of the Clinical Depression in Adolescents. In: International Conference on Engineering in Medicine and Biology Society, pp. 3723–3726 (2009)
Morken, G., Lilleeng, S., Linaker, L.M.: Seasonal Variation in Suicides and in Admissions to Hospital for Mania and Depression. Journal of Affective Disorders 69, 39–45 (2002)
Neuman, Y., Kedma, G., Cohen, Y., Nave, O.: Using Web-Intelligence for Excavating the Emerging Meaning of Target-Concepts. In: Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 22–25 (2010)
PTT, http://www.ptt.cc/index.html (retrieved June 27, 2011)
Sanchez, M.H., Vergyri, D., Ferrer, L., Richey, C., Garcia, P., Knoth, B., Jarrold, W.: Using Prosodic and Spectral Features in Detecting Depression in Elderly Males. In: INTERSPEECH, pp. 3001–3004 (2011)
Saraceno, B.: The WHO World Health Report 2001 on mental health. Epidemiol. Psychiatr. Soc. 11, 83–87 (2002)
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Shen, YC., Kuo, TT., Yeh, IN., Chen, TT., Lin, SD. (2013). Exploiting Temporal Information in a Two-Stage Classification Framework for Content-Based Depression Detection. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_23
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DOI: https://doi.org/10.1007/978-3-642-37453-1_23
Publisher Name: Springer, Berlin, Heidelberg
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