A Random Forest Regression Method With Selected-Text Feature For Depression Assessment
Authors:
Open Access
- 23 October 2017
- conference paper
- Published by Association for Computing Machinery (ACM)
- p. 61-68
Abstract
Audio/visual and mood disorder cues have been recently explored to assist psychologists and psychiatrists in Depression Diagnosis. In this paper, we propose a random forest method with a Selected-Text feature which is according to the analysis on the transcript in different depressive levels. The files are consisted of sleep quality, PTSD/Depression Diagnostic, successive treatment, personal preference and feeling. Experiments are carried out on the Distress Analysis Interview Corpus-Wizard of Oz (DAIC-WOZ) database[6]. Comparing with results obtained with audio based, video based or multi-feature based cascade decision-level fusion features, Selected-Text feature based method has obtained very promising results on the development and test sets. The root mean square error (RMSE) reaches 4.7, and mean absolute error (MAE) reaches 3.9, which are better than the baseline result, i.e. 7.05/5.66.Keywords
Funding Information
- National Natural Science Foundation of China (61501035)
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