Presentation Date: Friday 07 December, 2018
Presenter: Jessica Ribeiro, PhD
For decades, our ability to predict suicidal thoughts and behaviors has been only marginally better than chance. Recently, however, there has been a sea change in suicide prediction science. Through the development of machine learning risk algorithms, our ability to predict has considerably improved. The objective of this webinar will be to provide an overview of machine learning, and its implications for suicide research, theory, and practice. To this end, we will: (1) introduce machine learning and its basic concepts; (2) overview common misconceptions about machine learning; (3) review existing literature base using machine learning within suicide research, and (4) discuss how machine learning stands to advance suicide science moving forward.
Reference:
Franklin, J. C., Ribeiro, J. D., Fox, K. R., Bentley, K. H., Kleiman, E. M., Huang, X., . . . Nock, M. K. (2017). Risk factors for suicidal thoughts and behaviors: A meta-analysis of 50 years of research. Psychological Bulletin, 143(2), 187-232. http://dx.doi.org/10.1037/bul0000084