Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention

TitleKnowledge-aware Assessment of Severity of Suicide Risk for Early Intervention
Publication TypeConference Paper
Year of Publication2019
AuthorsManas Gaur, Amanuel Alambo, Sain, JP, Ugur Kursuncu, Thirunarayam, K, Ramakanth Kavuluru, Amit Sheth, Welton, RS, Jyotishman Pathak
Conference NameThe Web Conference 2019
PublisherAssociation for Computing Machinery
Conference LocationSan Francisco, CA, USA
KeywordsReddit; Mental Health; Suicide Risk; Semantic Social Computing
Abstract

Mental health illness such as depression is a significant risk factor for suicidal ideation and behaviors, including suicide attempts. A report by SAMHSA shows that 80% of the patients suffering from Borderline Personality Disorder (BPD) have suicidal behavior, 5-10% of whom commit suicide. While multiple initiatives have been developed and implemented for suicide prevention, a key challenge has been the social stigma associated with mental disorders, which deters patients from seeking help or sharing their experiences directly with others including clinicians. This is particularly true for teenagers and younger adults where suicide is the second highest cause of death in the U.S. Prior research involving surveys and
questionnaires (e.g., PHQ-9) for suicide risk prediction failed to provide a quantitative assessment of risk that informed timely clinical decision-making for intervention. Our interdisciplinary study concerns the use of Reddit as an unobtrusive data source for gleaning information about suicidal tendencies and other related mental health conditions afflicting depressed users. We provide details of our learning framework that incorporates domain-specific knowledge to predict the severity of suicide risk for an individual. Our approach involves developing a suicide risk severity lexicon using medical knowledge bases and suicide ontology to detect cues relevant to suicidal thoughts and actions, and using language modeling, medical entity recognition, and normalization, negation detection to interpret posts of 2181 redditors that have discussed or implied suicidal ideation, behavior, or attempt. Given the importance of clinical knowledge, our gold standard dataset of 500 redditors (out of 2181) was developed by four practicing psychiatrists following the guidelines outlined in Columbia Suicide Severity Rating Scale (C-SSRS), with the pairwise annotator agreement of 0.79 and group-wise agreement of 0.73. Compared to the existing four-label classification scheme (no risk, low risk, moderate risk, and high risk), our proposed C-SSRS-based 5-label classification scheme distinguishes people who are supportive, from those who show different severity of suicidal tendency concerning ideation, behavior, and attempt. Our 5-label classification scheme outperforms the state-of-the-art schemes by 4.2% and 12.5% in graded recall and perceived risk measure, respectively. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, as opposed to SVM-linear that is used in the state-of-the-art.

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Citation:
Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit Sheth, Randon S. Welton and Jyotishman Pathak. "Knowledge-aware Assessment of Severity of Suicide Risk for Early Intervention". The Web Conference 2019. San Francisco, California.

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