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1. Event Detection for Suicide Understanding

   https://doi.org/10.18653/v1/2022.findings-naacl.150  

   Guzman-Nateras, Luis; Lai, Viet; Pouran Ben Veyseh, Amir; Dernoncourt, Franck; Nguyen, Thien Huu (July 2022, Findings of the Association for Computational Linguistics: NAACL 2022)
2. Nanoreporter of an Enzymatic Suicide Inactivation Pathway

   https://doi.org/10.1021/acs.nanolett.0c01858  

   Yaari, Zvi; Cheung, Justin M.; Baker, Hanan A.; Frederiksen, Rune S.; Jena, Prakrit V.; Horoszko, Christopher P.; Jiao, Fang; Scheuring, Simon; Luo, Minkui; Heller, Daniel A. (November 2020, Nano Letters)

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3. Stories That Heal: Characterizing and Supporting Narrative for Suicide Bereavement

   https://doi.org/10.1609/icwsm.v18i1.31319  

   Doyle, Dylan Thomas; Ghosh, Jay K; Suchocki, Reece; Keegan, Brian C; Voida, Stephen; Brubaker, Jed R (May 2024, Proceedings of the International AAAI Conference on Web and Social Media)

   Clinical group bereavement therapy often promotes narrative sharing as a therapeutic intervention to facilitate grief processing. Increasingly, people turn to social media to express stories of loss and seek support surrounding bereavement experiences, specifically, the loss of loved ones from suicide. This paper reports the results of a computational linguistic analysis of narrative expression within an online suicide bereavement support community. We identify distinctive characteristics of narrative posts (compared to non-narrative posts) in linguistic style. We then develop and validate a machine-learning model for tagging narrative posts at scale and demonstrate the utility of applying this machine-learning model to a more general grief support community. Through comparison, we validate our model's narrative tagging accuracy and compare the proportion of narrative posts between the two communities we have analyzed. Narrative posts make up about half of all total posts in these two grief communities, demonstrating the importance of narrative posts to grief support online. Finally, we consider how the narrative tagging tool presented in this study can be applied to platform design to more effectively support people expressing the narrative sharing of grief in online grief support spaces. 

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4. Analyzing Social Factors to Enhance Suicide Prevention Across Population Groups

   https://doi.org/10.1109/ichi61247.2024.00032  

   Xu, Richard Li; Wang, Song; Wang, Zewei; Zhang, Yuhan; Xiao, Yunyu; Pathak, Jyotishman; Hodge, David; Leng, Yan; Watkins, S Craig; Ding, Ying; et al (June 2024, IEEE)

   Social factors like family background, education level, financial status, and stress can impact public health outcomes, such as suicidal ideation. However, the analysis of social factors for suicide prevention has been limited by the lack of up-to-date suicide reporting data, variations in reporting practices, and small sample sizes. In this study, we analyzed 172,629 suicide incidents from 2014 to 2020 utilizing the National Violent Death Reporting System Restricted Access Database (NVDRS-RAD). Logistic regression models were developed to examine the relationships between demographics and suicide-related circumstances. Trends over time were assessed, and Latent Dirichlet Allocation (LDA) was used to identify common suiciderelated social factors. Mental health, interpersonal relationships, mental health treatment and disclosure, and school/work-related stressors were identified as the main themes of suicide-related social factors. This study also identified systemic disparities across various population groups, particularly concerning Black individuals, young people aged under 24, healthcare practitioners, and those with limited education backgrounds, which shed light on potential directions for demographic-specific suicidal interventions. 

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5. Machine learning for suicide risk prediction in children and adolescents with electronic health records

   https://doi.org/10.1038/s41398-020-01100-0  

   Su, Chang; Aseltine, Robert; Doshi, Riddhi; Chen, Kun; Rogers, Steven C.; Wang, Fei (December 2020, Translational Psychiatry)

   null (Ed.)

   Abstract Accurate prediction of suicide risk among children and adolescents within an actionable time frame is an important but challenging task. Very few studies have comprehensively considered the clinical risk factors available to produce quantifiable risk scores for estimation of short- and long-term suicide risk for pediatric population. In this paper, we built machine learning models for predicting suicidal behavior among children and adolescents based on their longitudinal clinical records, and determining short- and long-term risk factors. This retrospective study used deidentified structured electronic health records (EHR) from the Connecticut Children’s Medical Center covering the period from 1 October 2011 to 30 September 2016. Clinical records of 41,721 young patients (10–18 years old) were included for analysis. Candidate predictors included demographics, diagnosis, laboratory tests, and medications. Different prediction windows ranging from 0 to 365 days were adopted. For each prediction window, candidate predictors were first screened by univariate statistical tests, and then a predictive model was built via a sequential forward feature selection procedure. We grouped the selected predictors and estimated their contributions to risk prediction at different prediction window lengths. The developed predictive models predicted suicidal behavior across all prediction windows with AUCs varying from 0.81 to 0.86. For all prediction windows, the models detected 53–62% of suicide-positive subjects with 90% specificity. The models performed better with shorter prediction windows and predictor importance varied across prediction windows, illustrating short- and long-term risks. Our findings demonstrated that routinely collected EHRs can be used to create accurate predictive models for suicide risk among children and adolescents. 

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