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

ABSTRACT: 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.

SUBMITTER: Macalli M

PROVIDER: S-EPMC8206419 | biostudies-literature |

REPOSITORIES: biostudies-literature

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A machine learning analysis of risk and protective factors of suicidal thoughts and behaviors in college students.

Project description:ObjectiveTo identify robust and reproducible factors associated with suicidal thoughts and behaviors (STBs) in college students.Methods356 first-year university students completed a large battery of demographic and clinically-relevant self-report measures during the first semester of college and end-of-year (n = 228). Suicide Behaviors Questionnaire-Revised (SBQ-R) assessed STBs. A machine learning (ML) pipeline using stacking and nested cross-validation examined correlates of SBQ-R scores.Results9.6% of students were identified at significant STBs risk by the SBQ-R. The ML algorithm explained 28.3% of variance (95%CI: 28-28.5%) in baseline SBQ-R scores, with depression severity, social isolation, meaning and purpose in life, and positive affect among the most important factors. There was a significant reduction in STBs at end-of-year with only 1.8% of students identified at significant risk.ConclusionAnalyses replicated known factors associated with STBs during the first semester of college and identified novel, potentially modifiable factors including positive affect and social connectedness.

| S-EPMC8782938 | biostudies-literature

First onset of suicidal thoughts and behaviours in college.

Project description:BackgroundCollege students are a worldwide increasing group of young people at risk for suicidal thoughts and behaviours (STB). However, no previous studies have prospectively investigated the first onset of STB during the college period.MethodsUsing longitudinal data from the Leuven College Surveys, 2337 (response rate [RR]=66.6%) incoming freshmen provided baseline data on STB, parental psychopathology, childhood-adolescent traumatic experiences, 12-month risk for mental disorders, and 12-month stressful experiences. A total of 1253 baseline respondents provided data on 12-month STB in a two-year annual follow-up survey (conditional RR=53.6%; college dropout adjusted conditional RR=70.2%).ResultsOne-year incidence of first-onset STB was 4.8-6.4%. Effect sizes of the included risk factors varied considerably whether viewed from individual-level (ORs=1.91-17.58) or population-level perspective (PARPs=3.4-34.3%). Dating violence prior to the age of 17, physical abuse prior to the age of 17, and 12-month betrayal by someone else than the partner were most strong predictors for first-onset suicidal ideation (ORs=4.23-12.25; PARPs=8.7-27.1%) and plans (ORs=6.57-17.58; PARPs=15.2-34.3%). Multivariate prediction (AUC=0.84-0.91) revealed that 50.7-65.7% of first-onset STB cases were concentrated in the 10% at highest predicted risk.LimitationsAs this is a first investigation of STB onset in college, future studies should use validation samples to test the accuracy of our multivariate prediction model.ConclusionsThe first onset of STB in college appears to be higher than in the general population. Screening at college entrance is a promising strategy to identify those students at highest prospective risk, enabling the cost-efficient clinical assessment of young adults in college.

| S-EPMC5460371 | biostudies-literature

A machine learning approach for predicting suicidal ideation in post stroke patients.

Project description:Currently, the identification of stroke patients with an increased suicide risk is mainly based on self-report questionnaires, and this method suffers from a lack of objectivity. This study developed and validated a suicide ideation (SI) prediction model using clinical data and identified SI predictors. Significant variables were selected through traditional statistical analysis based on retrospective data of 385 stroke patients; the data were collected from October 2012 to March 2014. The data were then applied to three boosting models (Xgboost, CatBoost, and LGBM) to identify the comparative and best performing models. Demographic variables that showed significant differences between the two groups were age, onset, type, socioeconomic, and education level. Additionally, functional variables also showed a significant difference with regard to ADL and emotion (p < 0.05). The CatBoost model (0.900) showed higher performance than the other two models; and depression, anxiety, self-efficacy, and rehabilitation motivation were found to have high importance. Negative emotions such as depression and anxiety showed a positive relationship with SI and rehabilitation motivation and self-efficacy displayed an inverse relationship with SI. Machine learning-based SI models could augment SI prevention by helping rehabilitation and medical professionals identify high-risk stroke patients in need of SI prevention intervention.

| S-EPMC9508242 | biostudies-literature

Suicidal Thoughts and Behaviors Among First-Year College Students: Results From the WMH-ICS Project.

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| S-EPMC6444360 | biostudies-literature

Predicting Suicidal Ideation in College Students with Mental Health Screening Questionnaires.

Project description:ObjectiveThe present study aimed to identify risk factors for future SI and to predict individual-level risk for future or persistent SI among college students.MethodsMental health check-up data collected over 3 years were retrospectively analyzed. Students were categorized as suicidal ideators and non-ideators at baseline. Logistic regression analyses were performed separately for each group, and the predicted probability for each student was calculated.ResultsStudents likely to exhibit future SI had higher levels of mental health problems, including depression and anxiety, and significant risk factors for future SI included depression, current SI, social phobia, alcohol problems, being female, low self-esteem, and number of close relationships and concerns. Logistic regression models that included current suicide ideators revealed acceptable area under the curve (AUC) values (0.7-0.8) in both the receiver operating characteristic (ROC) and precision recall (PR) curves for predicting future SI. Predictive models with current suicide non-ideators revealed an acceptable level of AUCs only for ROC curves.ConclusionSeveral factors such as low self-esteem and a focus on short-term rather than long-term outcomes may enhance the prediction of future SI. Because a certain range of SI clearly necessitates clinical attention, further studies differentiating significant from other types of SI are necessary.

| S-EPMC6259005 | biostudies-literature

Predicting short-term suicidal thoughts in adolescents using machine learning: developing decision tools to identify daily level risk after hospitalization.

Project description:BackgroundMobile technology offers unique opportunities for monitoring short-term suicide risk in daily life. In this study of suicidal adolescent inpatients, theoretically informed risk factors were assessed daily following discharge to predict near-term suicidal ideation and inform decision algorithms for identifying elevations in daily level risk, with implications for real-time suicide-focused interventions.MethodsAdolescents (N = 78; 67.9% female) completed brief surveys texted daily for 4 weeks after discharge (n = 1621 observations). Using multi-level classification and regression trees (CARTSs) with repeated 5-fold cross-validation, we tested (a) a simple prediction model incorporating previous-day scores for each of 10 risk factors, and (b) a more complex model incorporating, for each of these factors, a time-varying person-specific mean over prior days together with deviation from that mean. Models also incorporated missingness and contextual (study week, day of the week) indicators. The outcome was the presence/absence of next-day suicidal ideation.ResultsThe best-performing model (cross-validated AUC = 0.86) was a complex model that included ideation duration, hopelessness, burdensomeness, and self-efficacy to refrain from suicidal action. An equivalent model that excluded ideation duration had acceptable overall performance (cross-validated AUC = 0.78). Models incorporating only previous-day scores, with and without ideation duration (cross-validated AUC of 0.82 and 0.75, respectively), showed relatively weaker performance.ConclusionsResults suggest that specific combinations of dynamic risk factors assessed in adolescents' daily life have promising utility in predicting next-day suicidal thoughts. Findings represent an important step in the development of decision tools identifying short-term risk as well as guiding timely interventions sensitive to proximal elevations in suicide risk in daily life.

| S-EPMC9814182 | biostudies-literature

Predicting inmate suicidal behavior with an interpretable ensemble machine learning approach in smart prisons.

Project description:The convergence of smart technologies and predictive modelling in prisons presents an exciting opportunity to revolutionize the monitoring of inmate behaviour, allowing for the early detection of signs of distress and the effective mitigation of suicide risks. While machine learning algorithms have been extensively employed in predicting suicidal behaviour, a critical aspect that has often been overlooked is the interoperability of these models. Most of the work done on model interpretations for suicide predictions often limits itself to feature reduction and highlighting important contributing features only. To address this research gap, we used Anchor explanations for creating human-readable statements based on simple rules, which, to our knowledge, have never been used before for suicide prediction models. We also overcome the limitation of anchor explanations, which create weak rules on high-dimensionality datasets, by first reducing data features with the help of SHapley Additive exPlanations (SHAP). We further reduce data features through anchor interpretations for the final ensemble model of XGBoost and random forest. Our results indicate significant improvement when compared with state-of-the-art models, having an accuracy and precision of 98.6% and 98.9%, respectively. The F1-score for the best suicide ideation model appeared to be 96.7%.

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Lifetime and 12-month treatment for mental disorders and suicidal thoughts and behaviors among first year college students.

Project description:ObjectivesMental disorders and suicidal thoughts and behaviors (STB) are common and burdensome among college students. Although available evidence suggests that only a small proportion of the students with these conditions receive treatment, broad-based data on patterns of treatment are lacking. The aim of this study is to examine the receipt of mental health treatment among college students cross-nationally.MethodsWeb-based self-report surveys were obtained from 13,984 first year students from 19 colleges in eight countries across the world as part of the World Health Organization's World Mental Health-International College Student Initiative. The survey assessed lifetime and 12-month common mental disorders/STB and treatment of these conditions.ResultsLifetime and 12-month treatment rates were very low, with estimates of 25.3-36.3% for mental disorders and 29.5-36.1% for STB. Treatment was positively associated with STB severity. However, even among severe cases, lifetime and 12-month treatment rates were never higher than 60.0% and 45.1%, respectively.ConclusionsHigh unmet need for treatment of mental disorders and STB exists among college students. In order to resolve the problem of high unmet need, a reallocation of resources may focus on innovative, low-threshold, inexpensive, and scalable interventions.

| S-EPMC6877191 | biostudies-literature

Predicting acute suicidal ideation on Instagram using ensemble machine learning models.

Project description:IntroductionOnline social networking data (SN) is a contextually and temporally rich data stream that has shown promise in the prediction of suicidal thought and behavior. Despite the clear advantages of this digital medium, predictive modeling of acute suicidal ideation (SI) currently remains underdeveloped. SN data, in conjunction with robust machine learning algorithms, may offer a promising way forward.MethodsWe applied an ensemble machine learning model on a previously published dataset of adolescents on Instagram with a prior history of lifetime SI (N = 52) to predict SI within the past month. Using predictors that capture language use and activity within this SN, we evaluated the performance of our out-of-sample, cross-validated model against previous efforts and leveraged a model explainer to further probe relative predictor importance and subject-level phenomenology.ResultsLinguistic and SN data predicted acute SI with an accuracy of 0.702 (sensitivity = 0.769, specificity = 0.654, AUC = 0.775). Model introspection showed a higher proportion of SN-derived predictors with substantial impact on prediction compared with linguistic predictors from structured interviews. Further analysis of subject-specific predictor importance uncovered potentially informative trends for future acute SI risk prediction.ConclusionApplication of ensemble learning methodologies to SN data for the prediction of acute SI may mitigate the complexities and modeling challenges of SI that exist within these time scales. Future work is needed on larger, more heterogeneous populations to fine-tune digital biomarkers and more robustly test external validity.

| S-EPMC8350610 | biostudies-literature

Suicidal thoughts and behaviors among college students and same-aged peers: results from the World Health Organization World Mental Health Surveys.

Project description:PurposeThe primary aims are to (1) obtain representative prevalence estimates of suicidal thoughts and behaviors (STB) among college students worldwide and (2) investigate whether STB is related to matriculation to and attrition from college.MethodsData from the WHO World Mental Health Surveys were analyzed, which include face-to-face interviews with 5750 young adults aged 18-22 spanning 21 countries (weighted mean response rate = 71.4%). Standardized STB prevalence estimates were calculated for four well-defined groups of same-aged peers: college students, college attriters (i.e., dropouts), secondary school graduates who never entered college, and secondary school non-graduates. Logistic regression assessed the association between STB and college entrance as well as attrition from college.ResultsTwelve-month STB in college students was 1.9%, a rate significantly lower than same-aged peers not in college (3.4%; OR 0.5; p < 0.01). Lifetime prevalence of STB with onset prior to age 18 among college entrants (i.e., college students or attriters) was 7.2%, a rate significantly lower than among non-college attenders (i.e., secondary school graduates or non-graduates; 8.2%; OR 0.7; p = 0.03). Pre-matriculation onset STB (but not post-matriculation onset STB) increased the odds of college attrition (OR 1.7; p < 0.01).ConclusionSTB with onset prior to age 18 is associated with reduced likelihood of college entrance as well as greater attrition from college. Future prospective research should investigate the causality of these associations and determine whether targeting onset and persistence of childhood-adolescent onset STB leads to improved educational attainment.

| S-EPMC5896296 | biostudies-literature

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