Understanding, Predicting, and Preventing Suicide: Recent Advances Using Digital and Computational Methods
Matthew K. Nock & Shirley B. Wang
What the paper says
Suicide is among the most perplexing of all human behaviors. It has been a leading cause of death for decades, and despite significant study it continues unabated. Over the past few years, the development of new digital and computational methods has provided tools that are helping to overcome many long-standing challenges to studying suicide. Here we review recent advances in the understanding, prediction, and prevention of suicidal behaviors using such methods. Examples include the use of mathematical and computational modeling to build and test more precise theories of suicidal thoughts and behaviors, large-scale electronic databases to better detect and predict those at risk for suicide (e.g., health-care networks, social media, and other web-based platforms), smartphones and wearable biosensors to identify person-specific high-risk periods, and digital devices and platforms to deliver and test just-in-time adaptive interventions. Although suicide is a long-standing problem, these advances are facilitating significant progress and hope for the future of suicide prevention.
1 citation
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.16 × 0.4 = 0.06 |
| M · momentum | 0.53 × 0.15 = 0.08 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.