References
- Aggarwal, C. C. (2018). Neural networks and deep learning. Springer International Publishing.
- Ammerman, B. A., Steinberg, L., & McCloskey, M. S. (2018). Risk-taking behavior and suicidality: The unique role of adolescent drug use. Journal of Clinical Child and Adolescent Psychology, 47(1), 131–141. https://doi.org/https://doi.org/10.1080/15374416.2016.1220313
- Bauer, E., & Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 36(1/2), 105–139. https://doi.org/https://doi.org/10.1023/A:1007515423169
- Berman, A. L., Jobes, D. A., & Silverman, M. M. (2006). Adolescent suicide: Assessment and intervention (2nd ed.). American Psychological Association.
- Bolin, J., & Finch, W. (2014). Supervised classification in the presence of misclassified training data: A Monte Carlo simulation study in the three group case. Frontiers in Psychology, 5(118), 118. https://doi.org/https://doi.org/10.3389/fpsyg.2014.00118
- Breiman, L. (2001). Random forests. Machine Learning, 45(1), 5–32. https://doi.org/https://doi.org/10.1023/A:1010933404324
- Bryan, C. J., & Rudd, M. D. (2018). Nonlinear change processes during psychotherapy characterize patients who have made multiple suicide attempts. Suicide and Life Threatening Behavior, 48(4), 386–400. https://doi.org/https://doi.org/10.1111/sltb.12361
- Camino, R., Hammerschmidt, C., & State, R. (2018). Generating multi-categorical samples with generative adversarial networks [Paper presentation]. Presented at the ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models, Stockholm, Sweden. arXiv:1807.01202v2
- Centers for Disease Control and Prevention (CDC). (2016). National Suicide Statistics. https://www.cdc.gov/violenceprevention/suicide/statistics/index.html
- Centers for Disease Control and Prevention (CDC). (2017). Suicide: Risk and Protective Factors. https://www.cdc.gov/violenceprevention/suicide/riskprotectivefactors.html
- Centers for Disease Control and Prevention (CDC). (2018a). Youth Risk Behavior Survey Questionnaire. https://www.cdc.gov/yrbs
- Centers for Disease Control and Prevention (CDC). (2018b). Youth Risk Behavior Surveillance System (YRBS). http://www.cdc.gov/HealthyYouth/yrbs/index.htm.
- Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Artificial Intelligence Research, 16, 321–357. https://doi.org/https://doi.org/10.1613/jair.953
- Chen, X., Wang, M., & Zhang, H. (2011). The use of classification trees for bioinformatics. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 1(1), 55–63. https://doi.org/https://doi.org/10.1002/widm.14
- Chipman, H. A., George, E. I., & McCulloch, R. E. (2010). BART: Bayesian additive regression trees. The Annals of Applied Statistics, 4(1), 266–298. https://doi.org/https://doi.org/10.1214/09-AOAS285
- Choi, E., Biswal, S., Malin, B., Duke, J., Stewart, W. F., & Sun, J. (2017). Generating multi-label discrete patient records using generative adversarial networks [Paper presentation]. Proceedings of Machine Learning for Healthcare, Boston, MA, 2017. arXiv:1703.06490v3
- di Giacomo, E., Krausz, M., Colmegna, F., Aspesi, F., & Clerici, M. (2018). Estimating the risk of attempted suicide among sexual minority youths: A systematic review and meta-analysis. JAMA Pediatrics, 172(12), 1145–1152. https://doi.org/https://doi.org/10.1001/jamapediatrics.2018.2731
- Dietterich, T. G. (2000). An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization. Machine Learning, 40(2), 139–157. https://doi.org/https://doi.org/10.1023/A:1007607513941
- Fernández, A., Garcia, S., Herrera, F., & Chawla, N. V. (2018). SMOTE for learning from imbalanced data: Progress and challenges, marking the 15-year anniversary. Journal of Artificial Intelligence Research, 61, 863–905. https://doi.org/https://doi.org/10.1613/jair.1.11192
- Finch, W., Bolin, J., & Kelley, K. (2014). Group membership prediction when known groups consist of unknown subgroups: A Monte Carlo comparison of methods. Frontiers in Psychology, 5(337), 337. https://doi.org/https://doi.org/10.3389/fpsyg.2014.00337
- Freund, Y., & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. https://doi.org/https://doi.org/10.1006/jcss.1997.1504
- Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. B. (2013). Bayesian data analysis. Chapman and Hall/CRC.
- Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. In Z. Ghahramani, M. Welling, C. Cortes, N. D. Lawrence, & K. Q. Weinberger (Eds.), Proceedings of advances in neural information processing systems (pp. 2672–2680). Conference on Neural Information Processing Systems.
- Guan, K., Fox, K. R., & Prinstein, M. J. (2012). Nonsuicidal self-injury as a time-invariant predictor of adolescent suicide ideation and attempts in a diverse community sample. Journal of Consulting and Clinical Psychology, 80(5), 842–849. https:// https://doi.org/https://doi.org/10.1037/a0029429
- Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36. https://doi.org/https://doi.org/10.1148/radiology.143.1.7063747
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Science & Business Media.
- Horning, N. (2010, December). Random forests: An algorithm for image classification and generation of continuous fields data sets. In V. Rahavan (Ed.), Proceedings of the International Conference on Geoinformatics for Spatial Infrastructure Development in Earth and Allied Sciences (Vol. 911), Osaka, Japan.
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112). Springer.
- Japkowicz, N. (2000, June). The class imbalance problem: Significance and strategies. In Proceedings of the International Conference on Artificial Intelligence ICAI, Las Vegas, NV.
- Kang, R. P. (2017). National Hospital Ambulatory Medical Care Survey: 2017 emergency department summary tables. National Center for Health Statistics. https://www.cdc.gov/nchs/data/nhamcs/web_tables/2017_ed_web_tables-508.pdf.
- Kruschke, J. (2014). Doing Bayesian data analysis: A tutorial with R, JAGS, and Stan. Academic Press.
- Kurakin, A., Goodfellow, I. J., & Bengio, S. (2017). Adversarial machine learning at scale. In Y. Bengio & Y. LeCun (Eds.), Proceedings of 4th International Conference on Learning Representations. arXiv:1611.01236v2
- Lester, D., & Walker, R. L. (2006). The stigma for attempting suicide and the loss to suicide prevention efforts. Crisis, 27(3), 147–148. https://doi.org/https://doi.org/10.1027/0227-5910.27.3.147
- Lieberman, R., Poland, S., & Cassel, R. (2008). Best practices in suicide intervention. In A. Thomas, & J. Grimes (Eds.), Best practices in school psychology (5th ed., pp. 1457–1452). National Association of School Psychologists.
- Lindqvist, P., Johansson, L., & Karlsson, U. (2008). In the aftermath of teenage suicide: A qualitative study of the psychosocial consequences for the surviving family members. BMC Psychiatry, 8(1), 26–33. https://doi.org/https://doi.org/10.1186/1471-244X-8-26
- Ling, C. X., & Li, C. (1998, August). Data mining for direct marketing: Problems and solutions. In R. Agrawal & P. Stolorz (Eds.), Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining, Los Altos, CA (Vol. 98, pp. 73–79).
- Lunardon, N., Menardi, G., & Torelli, N. (2014). ROSE: A Package for. The R Journal, 6(1), 79–89. https://doi.org/https://doi.org/10.32614/RJ-2014-008
- McIntosh, J. L., Drapeau, C. W. (2012). U.S.A. suicide 2010: Official final data. https://suicidology.org/facts-and-statistics/.
- Menardi, G., & Torelli, N. (2014). Training and assessing classification rules with imbalanced data. Data Mining and Knowledge Discovery, 28(1), 92–122. https://doi.org/https://doi.org/10.1007/s10618-012-0295-5
- Mundt, J. C., Greist, J. H., Jefferson, J. W., Federico, M., Mann, J. J., & Posner, K. (2013). Prediction of suicidal behavior in clinical research by lifetime suicidal ideation and behavior ascertained by the electronic Columbia-Suicide Severity Rating Scale. Journal of Clinical Psychiatry, 74(9), 887–893. https://doi.org/https://doi.org/10.4088/JCP.13m08398
- Murphy, S. L., Xu, J. Q., Kochanek, K. D., Curtin, S. C., & Arias, E. (2017). Deaths: Final data for 2015. National Vital Statistics Reports: From the Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System, 66(6), 1–73.
- R Core Team. (2019). R: A language and environment for statistical computing. https://www.R-project.org/.
- Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., & Chen, X. (2016). Improved techniques for training gans. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, & R. Garnett (Eds.), Advances in neural information processing systems (pp. 2234–2242). Curran Associates Inc. arXiv:1606.03498v1
- Schilling, E. A., Aseltine, R. H., & James, A. (2016). The SOS suicide prevention program: Further evidence of efficacy and effectiveness. Prevention Science: The Official Journal of the Society for Prevention Research, 17(2), 157–166. https://doi.org/https://doi.org/10.1007/s11121-015-0594-3
- Springenberg, J. T. (2015). Unsupervised and semi-supervised learning with categorical generative adversarial networks. In Y. Bengio, & Y. LeCun (Eds.), Proceedings of 4th International Conference on Learning Representations. arXiv:1511.06390v2
- Steyerberg, E. W. (2019). Clinical prediction models. Springer International Publishing.
- Thanathamathee, P., & Lursinsap, C. (2013). Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and AdaBoost techniques. Pattern Recognition Letters, 34(12), 1339–1347. https://doi.org/https://doi.org/10.1016/j.patrec.2013.04.019
- Thompson, A. P., & McGrath, A. (2012). Subgroup differences and implications for contemporary risk-need assessment with juvenile offenders. Law and Human Behavior, 36(4), 345–355. https://doi.org/https://doi.org/10.1037/h0093930
- Torgo, L. (2016). Data mining with R: Learning with case studies. CRC Press.
- Tuisku, V., Kiviruusu, O., Pelkonen, M., Karlsson, L., Strandholm, T., & Marttunen, M. (2014). Depressed adolescents as young adults – Predictors of suicide attempt and non-suicidal self-injury during an 8-year follow-up. Journal of Affective Disorders, 152-154, 313–319. https://doi.org/https://doi.org/10.1016/j.jad.2013.09.031
- VanDerHeyden, A. M. (2013). Universal screening may not be for everyone: Using a threshold model as a smarter way to determine risk. School Psychology Review, 42(4), 402–414. https://doi.org/https://doi.org/10.1080/02796015.2013.12087462
- Walrath, C., Garraza, L. G., Reid, H., Goldston, D. B., & McKeon, R. (2015). Impact of the Garrett Lee Smith youth suicide prevention program on suicide mortality. American Journal of Public Health, 105(5), 986–993. https://doi.org/https://doi.org/10.2105/AJPH.2014.302496
- Wyman, P. A., Brown, C. H., LoMurray, M., Schmeelk-Cone, K., Petrova, M., Yu, Q., Walsh, M. S., Tu, X., & Wang, W. (2010). An outcome evaluation of the sources of strength suicide prevention program delivered by adolescent peer leaders in high schools. American Journal of Public Health, 100(9), 1653–1661. https://doi.org/https://doi.org/10.2105/AJPH.2009.190025
- Zhang, Y., Xiang, T., Hospedales, T. M., & Lu, H. (2018). Deep mutual learning [Paper presentation]. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT (pp. 4320–4328). https://doi.org/https://doi.org/10.1109/CVPR.2018.00454
- Zughrat, A., Mahfouf, M., Yang, Y. Y., & Thornton, S. (2014). Support vector machines for class imbalance rail data classification with bootstrapping-based over-sampling and under-sampling. IFAC Proceedings Volumes, 47(3), 8756–8761. https://folk.ntnu.no/skoge/prost/proceedings/ifac2014/proceedings.html