Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

Journal: The Journal of clinical psychiatry

Published Date: Apr 21, 2020

Abstract

OBJECTIVE: Machine learning algorithms in electronic medical records can classify patients by suicide risk, but no research has explored clinicians' perceptions of suicide risk flags generated by these algorithms, which may affect algorithm implementation. The objective of this study was to evaluate clinician perceptions of suicide risk flags.

Authors

Lily A Brown

3535 Market St, Ste 600 N, Philadelphia, PA 19104. lilybr@upenn.edu.
Kathy Benhamou

Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Alexis M May

Department of Psychology, Wesleyan University, Middletown, Connecticut, USA.
Wenting Mu

Department of Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania, USA.
Richard Berk

Department of Criminology and Department of Statistics, University of Pennsylvania, Philadelphia, Pennsylvania, USA.

Keywords

Adult Algorithms Attitude of Health Personnel Electronic Health Records False Negative Reactions False Positive Reactions Female Health Personnel Humans Machine Learning Male Physicians Risk Assessment Suicide Prevention

External Resources

View on PubMed Access via DOI PubMed (32316078)

Machine Learning Algorithms in Suicide Prevention: Clinician Interpretations as Barriers to Implementation.

Abstract

Authors

Keywords

External Resources

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