Using Artificial Intelligence in Suicide Risk Assessment

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Description

Suicide is a growing public health concern, not just nationally, but globally. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Because of that,  suicide detection strategies are shifting toward artificial intelligence platforms.  These platforms can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations.

In this interview, Dr. Sharp speaks with Dr. April Foreman and Dr. Tony Wood about the role of artificial intelligence in optimizing suicide risk prediction and behavior management. The two of them have an infectious energy and a clear passion for this incredibly important topic, and both of these qualities are on full display during this episode. Here are just a few of the things that are discussed:

  • Artificial intelligence in this context of suicide research
  • The true impact of risk factors in suicide assessment
  • Ethics of AI research
  • Things that can be done in clinical practice to help manage patients who may be at risk for suicide

This interview was published in 11/2020

Content Category: Assessment/Testing/Suicide

Format: Asynchronous, distance learning. Non-interactive. Recorded audio with transcript.

Target audience: Psychologists, mental health counselors, social workers, marriage and family therapists,  addiction counselors, nurses

This CE program is designated as beginner

Conflict of interest or commercial support disclaimer:  Dr. Tony Wood is the COO of Qntfy and the founder of #SPSM.