Leveraging data science to enhance suicide prevention research: a literature review

Abstract

ObjectiveThe purpose of this research is to identify how data science is applied in suicide prevention literature, describe the current landscape of this literature and highlight areas where data science may be useful for future injury prevention research.DesignWe conducted a literature review of injury prevention and data science in April 2020 and January 2021 in three databases.MethodsFor the included 99 articles, we extracted the following: (1) author(s) and year; (2) title; (3) study approach (4) reason for applying data science method; (5) data science method type; (6) study description; (7) data source and (8) focus on a disproportionately affected population.ResultsResults showed the literature on data science and suicide more than doubled from 2019 to 2020, with articles with individual-level approaches more prevalent than population-level approaches. Most population-level articles applied data science methods to describe (n=10) outcomes, while most individual-level articles identified risk factors (n=27). Machine learning was the most common data science method applied in the studies (n=48). A wide array of data sources was used for suicide research, with most articles (n=45) using social media and web-based behaviour data. Eleven studies demonstrated the value of applying data science to suicide prevention literature for disproportionately affected groups.ConclusionData science techniques proved to be effective tools in describing suicidal thoughts or behaviour, identifying individual risk factors and predicting outcomes. Future research should focus on identifying how data science can be applied in other injury-related topics.

Risk Factors of Computer Vision Syndrome and its Prevention

“…(17) In contrast, a study of call center employees found that 54.6% of them reported having a burning sensation, indicating a high prevalence of the condition. (18) The length of time spent using a computer is directly related to eye symptoms; longer sessions often lead to complaints that persist even after the work is completed. In our study, we discovered a direct relationship between computer use time (including time on a laptop and a mobile device) and computer vision syndrome symptoms.…”

Dynamic suicide topic modelling: Deriving population‐specific, psychosocial and time‐sensitive suicide risk variables from Electronic Health Record psychotherapy notes

“…Responding to this difficulty, machine learning algorithms—models that are designed to predict rare events—are increasingly used to derive complex networks of potential risk variables (Walsh et al, 2017). While related innovations have led to predictive gains, leading suicide prediction models have been critiqued for lacking optimal accuracy and population relevance (Belsher et al, 2019; Kessler, Bossarte, et al, 2019), small sample sizes, restricted potential risk variables, non‐appropriate analytic techniques, lack of clinical implications and ability to compare novel and existing methods (Kessler, 2019; Tsui et al, 2021; Wulz et al, 2022).…”

Text Analysis of Suicide Risk in Adolescents and Young Adults

“…Social media has the potential to support the early detection of suicide risk. Studies have shown that suicidal thoughts and behaviors can be detected from texts from social media (Ramirez-Cifuentes et al, 2020; Robinson et al, 2016; Tadesse et al, 2020; Wulz et al, 2021). Most AYAs, regardless of gender, socioeconomic status, or race and ethnicity, have access to a computer or own a smartphone to use the internet daily.…”