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Text mining methods for the characterisation of suicidal thoughts and behaviour.

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Summary

Natural language processing (NLP) effectively assesses suicidal risk by analyzing patient texts, outperforming traditional methods. This technology offers a promising, clinically applicable tool for real-time mental health monitoring and intervention.

Keywords:
Machine learningMobile healthNatural language processingSuicide attemptSuicide, Suicidal ideation

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Area of Science:

  • Psychiatry
  • Computational Linguistics
  • Mental Health Technology

Background:

  • Traditional suicidal risk assessment methods have limited predictive accuracy and clinical utility.
  • There is a need for innovative tools to enhance the early detection of self-injurious thoughts and behaviors.

Purpose of the Study:

  • To evaluate natural language processing (NLP) as a novel tool for assessing suicidal risk.
  • To analyze emotional content and suicidal ideation in psychiatric outpatients' free-text responses.

Main Methods:

  • Utilized the MEmind project, analyzing 2,838 psychiatric outpatients' anonymous responses to 'how are you feeling today?'.
  • Applied NLP to a corpus of 5,489 free-text documents (12,256 unique words) to determine emotional content and suicidal risk.
  • Compared NLP-derived risk scores against a direct question assessing suicidal ideation.

Main Results:

  • NLP achieved a high accuracy (ROC-AUC score of 0.9638) in classifying patients based on suicidal risk.
  • The analysis demonstrated NLP's capability to identify individuals with a lack of desire to live from their written expressions.

Conclusions:

  • NLP presents a highly accurate and promising method for assessing suicidal risk using patient-generated text.
  • This approach is readily applicable to clinical settings, enabling real-time communication and improved intervention strategies.