A machine learning approach predicts future risk to suicidal ideation from social media data

Abstract

Machine learning analysis of social media data represents a promising way to capture longitudinal environmental influences contributing to individual risk for suicidal thoughts and behaviors. Our objective was to generate an algorithm termed "Suicide Artificial Intelligence Prediction Heuristic (SAIPH)" capable of predicting future risk to suicidal thought by analyzing publicly available Twitter data. We trained a series of neural networks on Twitter data queried against suicide associated psychological constructs including burden, stress, loneliness, hopelessness, insomnia, depression, and anxiety. Using 512,526 tweets from N = 283 suicidal ideation (SI) cases and 3,518,494 tweets from 2655 controls, we then trained a random forest model using neural network outputs to predict binary SI status. The model predicted N = 830 SI events derived from an independent set of 277 suicidal ideators relative to N = 3159 control events in all non-SI individuals with an AUC of 0.88 (95% CI 0.86-0.90). Using an alternative approach, our model generates temporal prediction of risk such that peak occurrences above an individual specific threshold denote a~7 fold increased risk for SI within the following 10 days (OR = 6.7 ± 1.1, P = 9 × 10 −71). We validated our model using regionally obtained Twitter data and observed significant associations of algorithm SI scores with county-wide suicide death rates across 16 days in August and in October, 2019, most significantly in younger individuals. Algorithmic approaches like SAIPH have the potential to identify individual future SI risk and could be easily adapted as clinical decision tools aiding suicide screening and risk monitoring using available technologies.

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

“…As such, the use of artificial intelligence and machine learning offers new possibilities to significantly guide risk prediction and advance suicide prevention frameworks. Though recent studies yield promising findings [ 15 , 26 , 27 , 28 , 29 ], ML investigations for suicide prevention span diverse medical and computer science fields—challenging ease of review, dissemination, and impact. We therefore conducted a systematic review of empirical reports in this area, with a primary focus on the use of AI in suicide prevention.…”

Suicide and the agent–host–environment triad: leveraging surveillance sources to inform prevention

“…For example, Eichstaedt et al ( 2018 ) established that Facebook status updates predict medical-record-documented depression up to three months before diagnosis with fair accuracy (Eichstaedt et al, 2018 ). Online sources such as Twitter (Fahey, Boo, & Ueda, 2020 ; Roy et al, 2020 ) and Reddit (De Choudhury, Kiciman, Dredze, Coppersmith, & Kumar, 2016 ) have been leveraged for content and shifts in writing style, word choice, and other natural language indicators that could serve as sentinel signals of an emerging suicidal crisis, and could be used in epidemiological surveillance moving forward as the field and methods become more sophisticated. However, concerns about intrusion and privacy require careful ethical assessment as this field moves forward.…”

Relationships of coping styles and psychological distress among patients with insomnia disorder

“…Psychiatric distress was estimated using the Chinese version of SCL-90R [26]. The SCL-90R is a selfreporting, clinical symptom rating scale consisting of 90 items.…”