Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps

• Published in: Current psychiatry reports Vol. 20; no. 7; p. 51
• Main Authors: Torous, John, Larsen, Mark E., Depp, Colin, Cosco, Theodore D., Barnett, Ian, Nock, Matthew K., Firth, Joe
• Format: Journal Article
• Language: English
• Published: New York Springer US 01.07.2018
• Subjects: Medicine; Medicine & Public Health; Psychiatry; Psychiatry in the Digital Age (J Shore; Section Editor; Topical Collection on Psychiatry in the Digital Age; Smartphones; Algorithms; Mobile health; Machine learning; Mental health; Big data; Suicide; Apps
• ISSN: 1523-3812; 1535-1645
• DOI: 10.1007/s11920-018-0914-y

Purpose of Review As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been proposed, are in development, or are already available today. This review seeks to assess their clinical evidence and help the reader understand the current state of the field. Recent Findings Advances in smartphone sensing, machine learning methods, and mobile apps directed towards reducing suicide offer promising evidence; however, most of these innovative approaches are still nascent. Further replication and validation of preliminary results is needed. Summary Whereas numerous promising mobile and sensor technology based solutions for real time understanding, predicting, and caring for those at highest risk of suicide are being studied today, their clinical utility remains largely unproven. However, given both the rapid pace and vast scale of current research efforts, we expect clinicians will soon see useful and impactful digital tools for this space within the next 2 to 5 years.