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-ye...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2021-06-01
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| Series: | Scientific Reports |
| Online Access: | https://doi.org/10.1038/s41598-021-90728-z |
| _version_ | 1829488139062738944 |
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| author | Melissa Macalli Marie Navarro Massimiliano Orri Marie Tournier Rodolphe Thiébaut Sylvana M. Côté Christophe Tzourio |
| author_facet | Melissa Macalli Marie Navarro Massimiliano Orri Marie Tournier Rodolphe Thiébaut Sylvana M. Côté Christophe Tzourio |
| author_sort | Melissa Macalli |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-14T23:44:25Z |
| format | Article |
| id | doaj.art-e2ba1cd1809441ecb96b5fb22cf5bbe9 |
| institution | Directory Open Access Journal |
| issn | 2045-2322 |
| language | English |
| last_indexed | 2024-12-14T23:44:25Z |
| publishDate | 2021-06-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj.art-e2ba1cd1809441ecb96b5fb22cf5bbe92022-12-21T22:43:27ZengNature PortfolioScientific Reports2045-23222021-06-011111810.1038/s41598-021-90728-zA machine learning approach for predicting suicidal thoughts and behaviours among college studentsMelissa Macalli0Marie Navarro1Massimiliano Orri2Marie Tournier3Rodolphe Thiébaut4Sylvana M. Côté5Christophe Tzourio6Inserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxInserm, Bordeaux Population Health Research Center, UMR 1219, University of BordeauxAbstract 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.https://doi.org/10.1038/s41598-021-90728-z |
| spellingShingle | Melissa Macalli Marie Navarro Massimiliano Orri Marie Tournier Rodolphe Thiébaut Sylvana M. Côté Christophe Tzourio A machine learning approach for predicting suicidal thoughts and behaviours among college students Scientific Reports |
| title | A machine learning approach for predicting suicidal thoughts and behaviours among college students |
| title_full | A machine learning approach for predicting suicidal thoughts and behaviours among college students |
| title_fullStr | A machine learning approach for predicting suicidal thoughts and behaviours among college students |
| title_full_unstemmed | A machine learning approach for predicting suicidal thoughts and behaviours among college students |
| title_short | A machine learning approach for predicting suicidal thoughts and behaviours among college students |
| title_sort | machine learning approach for predicting suicidal thoughts and behaviours among college students |
| url | https://doi.org/10.1038/s41598-021-90728-z |
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