期刊
ARTIFICIAL INTELLIGENCE IN MEDICINE
卷 56, 期 1, 页码 19-25出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.artmed.2012.06.001
关键词
Depression; Mental health; Automatic screening; Natural language processing
资金
- Israel Ministry of Defense
Objective: Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge. Materials and method: The system implementing the methodology - Pedesis - harvests the Web for metaphorical relations in which depression is embedded and extracts the relevant conceptual domains describing it. This information is used by human experts for the construction of a depression lexicon. The lexicon is used to automatically evaluate the level of depression in texts or whether the text is dealing with depression as a topic. Results: Tested on three corpora of questions addressed to a mental health site the system provides 9% improvement in prediction whether the question is dealing with depression. Tested on a corpus of Blogs, the system provides 84.2% correct classification rate (p < .001) whether a post includes signs of depression. By comparing the system's prediction to the judgment of human experts we achieved an average 78% precision and 76% recall. Conclusion: Depression can be automatically screened in texts and the mental health system may benefit from this screening ability. (C) 2012 Elsevier B.V. All rights reserved.
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