Artificial Intelligence in Medicine 56 (2012) 19–25 Contents lists available at SciVerse ScienceDirect Artificial Intelligence in Medicine j o ur nal home page: www.elsevier.com/locate/aiim Proactive screening for depression through metaphorical and automatic text analysis Yair Neuman a,∗ , Yohai Cohen b,1 , Dan Assaf a , Gabbi Kedma a a Department of Education, Ben-Gurion University of the Negev, Beer-Sheva 84105, Israel b Gilasio Coding, Tel-Aviv, Israel a r t i c l e i n f o Article history: Received 14 December 2010 Received in revised form 22 March 2012 Accepted 13 June 2012 Keywords: Depression Mental health Automatic screening Natural language processing a b s t r a c t 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. © 2012 Elsevier B.V. All rights reserved. 1. Introduction The prevalence of depression in the Western society [1] puts heavy constraints on the ability of the health system to provide individual diagnosis and treatment. Therefore, despite the difficul- ties associated with screening and diagnosis of depression [2,3], a preliminary phase of screening for depression is inevitable. In this context, the expanding popularity of the Web and various forms of social media has introduced new platforms for addressing this challenge. Screening for depression through online questionnaires [4] was only a first step in this direction. However, this process is passive in the sense that the individual is fully responsible for getting access to and actively participating in the screening pro- cedure. Therefore, the price of using the online questionnaire is self-selection and the exclusion of relevant subjects. A different complementary and proactive approach may actively analyze texts written by individuals, such as posts published in their personal ∗ Corresponding author. Tel.: +972 8 6461844; fax: +972 8 6472897. E-mail addresses: yneuman@bgu.ac.il (Y. Neuman), yohai@gilasio.com (Y. Cohen). 1 Tel.: +972 54 7926 997. Blogs, and identify signs of depression in the text through automatic text analysis. The aim of this paper is to present a system that screens for depression in texts. The system is based on a novel approach for identifying the meaning associated with a target term [5] through metaphorical analysis. However, because of space limitations and the specific focus of the paper, this paper focuses only on providing empirical evidence for the system’s ability to screen for depression in texts. Such an application may be highly relevant, for instance, to mental health agencies seeking a tool for automatic screening for depression. The rationale of this idea is as follows. A mental health agency may provide subjects with free access to our screening sys- tem. Given the subject’s permission, the system may proactively and automatically screen for signs of depression in texts written by the subject, such as posts s(he) writes in her Blogs. When the system identifies signs of depression, it may inform the subject and offer the opportunity to complete a short online question- naire. If the questionnaire, as a second phase of screening, also identifies symptoms of depression then the subject is advised to consult a mental health expert for professional diagnosis and treat- ment. By using this graded procedure the mental health system may proactively and economically screen for depression in a massive population, before more exact, albeit more expensive, steps will be taken. 0933-3657/$ – see front matter © 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.artmed.2012.06.001