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A Lexical Network Approach for Identifying Suicidal Ideation in Clinical Interview Transcripts

  • Ulya Bayram
  • Ali A. Minai
  • John Pestian
Conference paper
Part of the Springer Proceedings in Complexity book series (SPCOM)

Abstract

Preventing suicide requires early identification of suicidal ideation. In this research, we propose an approach to evaluate whether an individual’s statements during a clinical interview can be classified as coming from a suicidal or non-suicidal mindset. To do so, we compare the statements with distinct lexical associative networks constructed from corpora of suicidal and control texts. Each node in these networks is a word, and the weight of the edge between every word pair indicates how strongly the words are associated in that corpus. Several metrics of association are evaluated in this work. Preliminary results show good classification performance with above 75% accuracy on novel test data.

Keywords

Lexical networks Suicidal ideation Suicide Clinical interviews Word association Spreading activation Classification 

Notes

Acknowledgment

We are grateful for the support of the University of Cincinnati College of Medicine, and the Department of Electrical Engineering and Computer Science. We are also grateful for the support of the Cincinnati Children’s Hospital Medical Center, and its division of Biomedical Informatics.

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of CincinnatiCincinnatiUSA
  2. 2.Department of Pediatrics and Cincinnati Children’s Hospital Medical CenterUniversity of CincinnatiCincinnatiUSA

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