Department of Education, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel.
Artif Intell Med. 2012 Sep;56(1):19-25. doi: 10.1016/j.artmed.2012.06.001. Epub 2012 Jul 6.
Proactive and automatic screening for depression is a challenge facing the public health system. This paper describes a system for addressing the above challenge.
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.
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.
Depression can be automatically screened in texts and the mental health system may benefit from this screening ability.
主动和自动筛查抑郁症是公共卫生系统面临的挑战。本文描述了一种应对上述挑战的系统。
实现该方法的系统——Pedesis——从网络中提取抑郁症相关的隐喻关系，并提取相关的描述概念领域。这些信息被人类专家用于构建“抑郁症词典”。该词典用于自动评估文本中的抑郁程度，或者文本是否将抑郁症作为主题。
在三个向心理健康网站提出的问题语料库上进行测试，该系统提高了 9%的预测能力，即问题是否与抑郁症有关。在一个博客语料库上进行测试，该系统对一篇文章是否包含抑郁迹象的正确分类率为 84.2%（p<.001）。通过将系统的预测与人类专家的判断进行比较，我们达到了平均 78%的准确率和 76%的召回率。
可以在文本中自动筛查抑郁症，心理健康系统可能受益于这种筛查能力。
