@article{ref1,
title="Detecting suicide risk using knowledge-aware natural language processing and counseling service data",
journal="Social science and medicine (1982)",
year="2021",
author="Xu, Zhongzhi and Xu, Yucan and Cheung, Florence and Cheng, Mabel and Lung, Daniel and Law, Yik Wa and Chiang, Byron and Zhang, Qingpeng and Yip, Paul S. F.",
volume="283",
number="",
pages="e114176-e114176",
abstract="RATIONALE: Detecting users at risk of suicide in text-based counseling services is essential to ensure that at-risk individuals are flagged and prioritized. <br><br>OBJECTIVE: The objective of this study is to develop a domain knowledge-aware risk assessment (KARA) model to improve our ability of suicide detection in online counseling systems. <br><br>METHODS: We obtained the largest known de-identified dataset from an emotional support system established in Hong Kong, comprising 5682 Cantonese conversations between help-seekers and counselors. Of those, 682 conversations disclosed crisis intentions of suicide. We constructed a suicide-knowledge graph, representing suicide-related domain knowledge as a computer-processible graph. Such knowledge graph was embedded into a deep learning model to improve its ability to identify help-seekers in crisis. As the baseline, a standard NLP model was applied to the same task. 80% of the study samples were randomly sampled to train model parameters. The remaining 20% were used for model validation. Evaluation metrics including precision, recall, and c-statistic were reported. <br><br>RESULTS: Both KARA and the baseline achieved high precision (0.984 and 0.951, shown in Table 2) and high recall (0.942 and 0.947) towards non-crisis cases. For crisis cases, however, KARA model achieved a much higher recall than the baseline (0.870 vs 0.791). The c-statistics of KARA and the baseline were 0.815 and 0.760, respectively. <br><br>CONCLUSION: KARA significantly outperformed standard NLP models, demonstrating good translational value and clinical relevance.<p /> <p>Language: en</p>",
language="en",
issn="0277-9536",
doi="10.1016/j.socscimed.2021.114176",
url="http://dx.doi.org/10.1016/j.socscimed.2021.114176"
}