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Related Concept Videos

SBAR II: Application of SBAR01:14

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Specialized care settings or centers are situated in convenient locations within the community and offer care to a specific group or population. They consist of daycare facilities, mental health facilities, rural health facilities, educational institutions, industries, shelters for the homeless, and rehabilitation facilities.
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Related Experiment Video

Updated: Oct 30, 2025

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Detecting suicide risk using knowledge-aware natural language processing and counseling service data.

Zhongzhi Xu1, Yucan Xu2, Florence Cheung2

  • 1School of Data Science, City University of Hong Kong, Hong Kong Special Administrative Region; Hong Kong Jockey Club Centre for Suicide Research and Prevention, The University of Hong Kong, Hong Kong Special Administrative Region.

Social Science & Medicine (1982)
|July 2, 2021
PubMed
Summary

A new model, KARA (knowledge-aware risk assessment), significantly improves suicide detection in online counseling. It identifies at-risk users more effectively than standard NLP, enhancing safety in digital mental health support.

Keywords:
Artificial intelligenceKnowledge graphNatural language processingOnline counseling servicesSuicide prevention

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Area of Science:

  • Natural Language Processing
  • Computational Linguistics
  • Mental Health Technology

Background:

  • Detecting individuals at risk of suicide in text-based counseling is crucial for timely intervention.
  • Existing methods require enhancement for improved accuracy and reliability.

Purpose of the Study:

  • To develop and evaluate a domain knowledge-aware risk assessment (KARA) model.
  • To enhance suicide detection capabilities within online counseling systems.

Main Methods:

  • Utilized the largest known de-identified dataset of Cantonese conversations from a Hong Kong emotional support system (5682 conversations).
  • Constructed a suicide-knowledge graph and embedded it into a deep learning model (KARA).
  • Compared KARA against a standard Natural Language Processing (NLP) model using 80% training and 20% validation data.

Main Results:

  • KARA demonstrated high precision (0.984) and recall (0.942) for non-crisis cases.
  • For crisis cases, KARA achieved significantly higher recall (0.870) compared to the baseline (0.791).
  • KARA reported a higher c-statistic (0.815) than the baseline (0.760).

Conclusions:

  • The KARA model significantly outperforms standard NLP models in suicide detection.
  • KARA shows strong translational value and clinical relevance for online counseling platforms.
  • This approach enhances the ability to identify and support help-seekers in crisis.