Proactive screening for depression through metaphorical and automatic text analysis

Yair Neuman, Yohai Cohen, Dan Assaf, Gabbi Kedma

Research output: Contribution to journalArticlepeer-review

39 Scopus citations

Abstract

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.

Original languageEnglish
Pages (from-to)19-25
Number of pages7
JournalArtificial Intelligence in Medicine
Volume56
Issue number1
DOIs
StatePublished - 1 Sep 2012

Keywords

  • Automatic screening
  • Depression
  • Mental health
  • Natural language processing

ASJC Scopus subject areas

  • Medicine (miscellaneous)
  • Artificial Intelligence

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