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

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

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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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Correspondence to Ulya Bayram .

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Bayram, U., Minai, A.A., Pestian, J. (2018). A Lexical Network Approach for Identifying Suicidal Ideation in Clinical Interview Transcripts. In: Morales, A., Gershenson, C., Braha, D., Minai, A., Bar-Yam, Y. (eds) Unifying Themes in Complex Systems IX. ICCS 2018. Springer Proceedings in Complexity. Springer, Cham. https://doi.org/10.1007/978-3-319-96661-8_17

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