Title |
Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps
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Published in |
Current Psychiatry Reports, June 2018
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DOI | 10.1007/s11920-018-0914-y |
Pubmed ID | |
Authors |
John Torous, Mark E. Larsen, Colin Depp, Theodore D. Cosco, Ian Barnett, Matthew K. Nock, Joe Firth |
Abstract |
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. 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. 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. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 10 | 29% |
United Kingdom | 6 | 17% |
Netherlands | 1 | 3% |
Australia | 1 | 3% |
New Zealand | 1 | 3% |
Germany | 1 | 3% |
France | 1 | 3% |
Canada | 1 | 3% |
Unknown | 13 | 37% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 17 | 49% |
Scientists | 11 | 31% |
Practitioners (doctors, other healthcare professionals) | 7 | 20% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 312 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Bachelor | 41 | 13% |
Researcher | 38 | 12% |
Student > Ph. D. Student | 32 | 10% |
Student > Master | 27 | 9% |
Student > Doctoral Student | 20 | 6% |
Other | 49 | 16% |
Unknown | 105 | 34% |
Readers by discipline | Count | As % |
---|---|---|
Psychology | 60 | 19% |
Computer Science | 30 | 10% |
Medicine and Dentistry | 26 | 8% |
Engineering | 21 | 7% |
Social Sciences | 15 | 5% |
Other | 35 | 11% |
Unknown | 125 | 40% |