Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study

Mens, Kasper van; Schepper, Cwm de; Wijnen, Ben; Koldijk, Saskia; Schnack, Hugo; Looff, Peter de; Lokkerbol, Joran; Wetherall, Karen; Cleare, Seonaid; O’Connor, Rory C.; Beurs, Derek de

doi:10.1016/j.jad.2020.03.081

Cited by 49 publications

(42 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As pointed out in a recent paper summarizing 50 years of research on STB, further research should shift from identification of risk factors associated with STB to focus on developing predictive algorithms using machine learning methods 13 . Such methods enable the inclusion of several risk and protective factors, while accounting for their potential interactions 14 , 15 , which is consistent with the shared concept that STB result from complex interactions between social, psychiatric, psychological, and environmental factors 16 .…”

Section: Introductionsupporting

confidence: 67%

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Macalli

Navarro

Orri

et al. 2021

Sci Rep

View full text Add to dashboard Cite

Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviours. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviours in a community sample of college students.

show abstract

Section: Introductionsupporting

confidence: 67%

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Macalli

Navarro

Orri

et al. 2021

Sci Rep

View full text Add to dashboard Cite

show abstract

“…As pointed out in a recent paper summarizing 50 years of research on STB, further research should shift from identi cation of risk factors associated with STB to focus on developing predictive algorithms using machine learning methods. 13 Such methods enable the inclusion of several risk and protective factors, while accounting for their potential interactions, 14,15 which is consistent with the shared concept that STB result from complex interactions between social, psychiatric, psychological, and environmental factors. 16 In this study we applied a machine learning method to develop an algorithm to predict STB in the next 12 months after baseline assessment using a large longitudinal cohort of French university students.…”

Section: Introductionsupporting

confidence: 55%

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

Macalli

Navarro

Orri

et al. 2021

Preprint

View full text Add to dashboard Cite

Suicidal thoughts and behaviors are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviors among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the French i-Share cohort, a longitudinal population-based study including 5066 volunteer students. To predict suicidal thoughts and behaviours at follow-up, we used random forests models with 70 potential predictors measured at baseline, including sociodemographic and familial characteristics, mental health and substance use. Model performance was measured using the area under the receiver operating curve (AUC), sensitivity, and positive predictive value. At follow-up, 17.4% of girls and 16.8% of boys reported suicidal thoughts and behaviors. The models achieved good predictive performance: AUC, 0.8; sensitivity, 79% for girls, 81% for boys; and positive predictive value, 40% for girls and 36% for boys. Among the 70 potential predictors, four showed the highest predictive power: 12-month suicidal thoughts, trait anxiety, depression symptoms, and self-esteem. We identified a parsimonious set of mental health indicators that accurately predicted one-year suicidal thoughts and behaviors in a community sample of college students.

show abstract

“…47,48 ) open a completely new way to consider a large number of predictors and their interaction simultaneously. A shift away from a focus on risk factors to a focus on risk algorithms has the potential to advance the field; yet research in this area is inconclusive 49 and the prediction of suicidal behaviour is likely to stay an approximation. 50 Limitations and strengths…”

Section: Discussionmentioning

confidence: 99%

Interpersonal theory of suicide: prospective examination

et al. 2020

View full text Add to dashboard Cite

Background The interpersonal theory of suicide (IPTS) is one of the most intensively researched contemporary theories on the development of suicidal ideation and behaviour. However, there is a lack of carefully conducted prospective studies. Aims To evaluate the main predictions of the IPTS regarding the importance of perceived burdensomeness, thwarted belongingness and capability for suicide in predicting future suicide attempts in a prospective design. Method Psychiatric in-patients (n = 308; 53.6% (n = 165) female; mean age 36.82 years, s.d. = 14.30, range 18–81) admitted for severe suicidal ideation (n = 145, 47.1%) or a suicide attempt completed self-report measures of thwarted belongingness, perceived burdensomeness, capability for suicide, hopelessness, depression and suicidal ideation as well as interviews on suicide intent and suicide attempts and were followed up for 12 months. Logistic regression and receiver operating characteristics (ROC) analysis were conducted. Results The interaction of perceived burdensomeness, thwarted belongingness and capability for suicide was not predictive of future suicide attempts, but perceived burdensomeness showed a significant main effect (z = 3.49, P < 0.01; OR = 2.34, 95% CI 1.59–3.58) and moderate performance in screening for future suicide attempts (area under the curve AUC = 0.729, P < 0.01). Conclusions The results challenge the theoretical validity of the IPTS and its clinical utility – at least within the methodological limitations of the current study. Yet, findings underscore the importance of perceived burdensomeness in understanding suicidal ideation and behaviour.

show abstract

Predicting future suicidal behaviour in young adults, with different machine learning techniques: A population-based longitudinal study

Cited by 49 publications

References 30 publications

A machine learning approach for predicting suicidal thoughts and behaviours among college students

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Predicting suicidal thoughts and behaviors among college students: a machine learning approach

Interpersonal theory of suicide: prospective examination

Contact Info

Product

Resources

About