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Citation |
Coppersmith G, Leary R, Crutchley P, Fine A. Biomed. Inform. Insights 2018; 10: e1178222618792860. |
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Affiliation |
Qntfy, Boston, MA, USA. |
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Copyright |
(Copyright © 2018, Libertas Academica) |
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DOI |
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PMID |
30158822 |
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PMCID |
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Abstract |
Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people's lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media-and the near-ubiquity of mobile devices used to access social media networks-offers new types of data for understanding the behavior of those who (attempt to) take their own lives and suggests new possibilities for preventive intervention. We demonstrate the feasibility of using social media data to detect those at risk for suicide. Specifically, we use natural language processing and machine learning (specifically deep learning) techniques to detect quantifiable signals around suicide attempts, and describe designs for an automated system for estimating suicide risk, usable by those without specialized mental health training (eg, a primary care doctor). We also discuss the ethical use of such technology and examine privacy implications. Currently, this technology is only used for intervention for individuals who have "opted in" for the analysis and intervention, but the technology enables scalable screening for suicide risk, potentially identifying many people who are at risk preventively and prior to any engagement with a health care system. This raises a significant cultural question about the trade-off between privacy and prevention-we have potentially life-saving technology that is currently reaching only a fraction of the possible people at risk because of respect for their privacy. Is the current trade-off between privacy and prevention the right one? Language: en |
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Keywords |
Suicide; data science; natural language processing; social media; suicide prevention; suicide screening |