Machine Learning-Based Suicide Risk Prediction Model for Suicidal Trajectory on Social Media Following Suicidal Mentions: Independent Algorithm Validation.
| Autor: | Kaminsky Z; University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.; Department of Cellular and Molecular Medicine, University of Ottawa, Ottawa, ON, Canada.; Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States.; Department of Mental Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States., McQuaid RJ; University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.; Department of Neuroscience, Carleton University, Ottawa, ON, Canada., Hellemans KG; Department of Neuroscience, Carleton University, Ottawa, ON, Canada., Patterson ZR; Department of Neuroscience, Carleton University, Ottawa, ON, Canada., Saad M; University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.; Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada., Gabrys RL; Department of Neuroscience, Carleton University, Ottawa, ON, Canada., Kendzerska T; Faculty of Medicine, University of Ottawa, Ottawa, ON, Canada.; The Ottawa Hospital Research Institute, University of Ottawa, Ottawa, ON, Canada., Abizaid A; Department of Neuroscience, Carleton University, Ottawa, ON, Canada., Robillard R; University of Ottawa Institute of Mental Health Research at The Royal, Ottawa, ON, Canada.; Department of Psychology, University of Ottawa, Ottawa, ON, Canada. |
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| Jazyk: | angličtina |
| Zdroj: | Journal of medical Internet research [J Med Internet Res] 2024 Dec 05; Vol. 26, pp. e49927. Date of Electronic Publication: 2024 Dec 05. |
| DOI: | 10.2196/49927 |
| Abstrakt: | Background: Previous efforts to apply machine learning-based natural language processing to longitudinally collected social media data have shown promise in predicting suicide risk. Objective: Our primary objective was to externally validate our previous machine learning algorithm, the Suicide Artificial Intelligence Prediction Heuristic (SAIPH), against external survey data in 2 independent cohorts. A second objective was to evaluate the efficacy of SAIPH as an indicator of changing suicidal ideation (SI) over time. The tertiary objective was to use SAIPH to evaluate factors important for improving or worsening suicidal trajectory on social media following suicidal mention. Methods: Twitter (subsequently rebranded as X) timeline data from a student survey cohort and COVID-19 survey cohort were scored using SAIPH and compared to SI questions on the Beck Depression Inventory and the Self-Report version of the Quick Inventory of Depressive Symptomatology in 159 and 307 individuals, respectively. SAIPH was used to evaluate changing SI trajectory following suicidal mentions in 2 cohorts collected using the Twitter application programming interface. Results: An interaction of the mean SAIPH score derived from 12 days of Twitter data before survey completion and the average number of posts per day was associated with quantitative SI metrics in each cohort (student survey cohort interaction β=.038, SD 0.014; F Conclusions: Taken together, our results not only validate the association of SAIPH with perceived stress, SI, and changing SI over time but also generate novel methods to evaluate the effects of social media interactions on changing suicidal trajectory. (©Zachary Kaminsky, Robyn J McQuaid, Kim GC Hellemans, Zachary R Patterson, Mysa Saad, Robert L Gabrys, Tetyana Kendzerska, Alfonso Abizaid, Rebecca Robillard. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 05.12.2024.) |
| Databáze: | MEDLINE |
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