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Innovative data science approaches to identify individuals, populations, and communities at high risk for suicide proceedings of a workshop

Emerging real-time data sources, together with innovative data science techniques and methods - including artificial intelligence and machine learning - can help inform upstream suicide prevention efforts. Select social media platforms have proactively deployed these methods to identify individual p...

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Bibliographic Details
Main Author: Amankwah, Francis
Corporate Authors: National Academies of Sciences, Engineering, and Medicine (U.S.) Forum on Mental Health and Substance Use Disorders, Innovative Data Science Approaches to Identify Individuals, Populations, and Communities at High Risk for Suicide (Workshop) (2022, Online)
Other Authors: Pool, Robert ([rapporteur]), Nass, Sharyl J. ([rapporteur])
Format: eBook
Language:English
Published: Washington, DC National Academies Press 2022, [2022]
Online Access:
Collection: National Center for Biotechnology Information - Collection details see MPG.ReNa
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520 |a Emerging real-time data sources, together with innovative data science techniques and methods - including artificial intelligence and machine learning - can help inform upstream suicide prevention efforts. Select social media platforms have proactively deployed these methods to identify individual platform users at high risk for suicide, and in some cases may activate local law enforcement, if needed, to prevent imminent suicide. To explore the current scope of activities, benefits, and risks of leveraging innovative data science techniques to help inform upstream suicide prevention at the individual and population level, the Forum on Mental Health and Substance Use Disorders of the National Academies of Sciences, Engineering, and Medicine convened a virtual workshop series consisting of three webinars held on April 28, May 12, and June 30, 2022. This Proceedings highlights presentations and discussions from the workshop 

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