Work

Machine Learning for Suicide Prediction and Prevention: Advances, Challenges, and Future Directions

Year: 2022

Type: book-chapter

Abstract: This chapter describes the role of machine learning in youth suicide prevention. Following a brief history of suicide prediction, research is reviewed demonstrating that machine learning can enhance suicide prediction beyond traditional clinical and statistical approaches. Strategies for internal and external model evaluation, methods for integrating model results into clinical decision-making processes, and ethical issues raised by building and implementing suicide prediction models are discussed. Finally, future directions for this work are highlighted, including the need for collaborative science and the importance of both data- and theory-driven computational methods.

Source: SpringerBriefs in psychology

DOI: 10.1007/978-3-031-06127-1_3

Authors: Shirley B. Wang, Walter Dempsey, Matthew K. Nock

Institutions: Harvard University, University of Michigan

Language: English

Topic: Suicide and Self-Harm Studies

Subfield: Clinical Psychology

Field: Psychology

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: hybrid

Funders: National Science Foundation, National Institute of Mental Health