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

By Manas Gaur, Amanuel Alambo, Joy Prakash Sain, Ugur Kursuncu, Krishnaprasad Thirunarayan, Ramakanth Kavuluru, Amit P. Sheth, R. Welton, Jyotishman Pathak. 2019

223 citations recorded by the source corpus.

Mental health illness such as depression is a significant risk factor for suicide ideation, behaviors, and attempts. A report by Substance Abuse and Mental Health Services Administration (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 US. 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. We also use language modeling, medical entity recognition and normalization and negation detection to create a dataset 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. Our 5-label classification scheme outperforms the state-of-the-art schemes by improving the graded recall by 4.2% and reducing the perceived risk measure by 12.5%. Convolutional neural network (CNN) provided the best performance in our scheme due to the discriminative features and use of domain-specific knowledge resources, in comparison to SVM-L that has been used in the state-of-the-art tools over similar dataset.

Subfields: Clinical Psychology, Social Psychology

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Citations in the corpus

Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning (2019, background): “For example, in the suicide risk prediction task, references to suicide-related terminology appear in social media posts of both victims as well as supportive listeners, and the existing NAMs fail to capture semantic relations between terms that help differentiate the suicidal user from a supportive”
A Survey on Knowledge Graphs: Representation, Acquisition, and Applications (2020, background): “[218] developed a rulebased classifier for knowledge-aware suicide risk assessment with a suicide risk severity lexicon incorporating medical knowledge bases and suicide ontology.”
Misogyny Detection in Twitter: a Multilingual and Cross-Domain Study (2020, unknown): “…for maximizing the “good” social impacts, while minimizing the likelihood of harm, e.g., suicidal ideation detection for early intervention Gaur et al. (2019) and recent works on the prevention of sexual harassment Khatua, E., and Khatua (2018), sexual discrimination Khatua, Cambria,…”
Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications (2019, methodology): “The recent work [58] incorporated external knowledge using knowledge bases and suicide ontology for knowledge-aware suicide risk assessment.”
Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions (2019, result): “In contrast to this literature, our work is grounded in the social science literature and incorporates domain-specific resources [30, 31, 52] in the model to better understand and detect extremist content using linguistic approaches.”
Natural language processing applied to mental illness detection: a narrative review (2022, methodology): “Lin et al. designed a CNN framework combined with a graph model to leverage tweet content and social interaction information 129 .”
Leveraging Large Language Models for Mental Health Prediction via Online Text Data (2023, background): “• CSSRS-Suicide [31]: This dataset contains posts from 15 mental health-related subreddits.”
Suicide Ideation Detection via Social and Temporal User Representations using Hyperbolic Learning (2021, methodology): “Contextual Models: Amongst models utilizing user’s historical tweeting activity, we note methods modeling user tweets as temporal sequence (DualContextBERT, Hyper-SOS ) outperform bag-oftweets based models (C-CNN, SDM).”