Ethical Considerations for Natural Language Processing (NLP) in Suicide Prevention Research

Friday, March 19, 10:00am - 11:30am (EDT)

Time shown in-04:00 America, New York

Center for Bioethics & Health Law Colloquium

Jamie Zelazny, PhD, MPH, RN
Assistant Professor of Health & Community Systems
School of Nursing, University of Pittsburgh

Abstract: This work-in-progress discussion will explore the ethical challenges that must be considered when designing and conducting research studies involving the use and implementation of suicide risk prediction algorithms. Natural language processing (NLP) and machine learning (ML) provide cutting edge tools to mine large data sets for the development of predictive algorithms. The application of these methods to electronic health record (EHR) information and social media (SM) data can potentially aid in developing algorithms able to predict risk for psychiatric illness and suicidality. Careful consideration of the ethical issues involved in the development and implementation of these methods is needed. Ethical concerns are inherent with the use of risk algorithms for the prediction of illness in general. These concerns are magnified in vulnerable populations, especially in those with mental health disorders or suicidality.

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Meeting ID: 989 5777 8593

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+1 267 831 0333 US (Philadelphia)
877 853 5247 US Toll-free
Meeting ID: 989 5777 8593

Add to Calendar 2021/03/19 10:00:00 2021/03/19 11:30:00 America/New_York Ethical Considerations for Natural Language Processing (NLP) in Suicide Prevention Research Center for Bioethics & Health Law Colloquium

Jamie Zelazny, PhD, MPH, RN
Assistant Professor of Health & Community Systems
School of Nursing, University of Pittsburgh

Abstract: This work-in-progress discussion will explore the ethical challenges that must be considered when designing and conducting research studies involving the use and implementation of suicide risk prediction algorithms. Natural language processing (NLP) and machine learning (ML) provide cutting edge tools to mine large data sets for the development of predictive algorithms. The application of these methods to electronic health record (EHR) information and social media (SM) data can potentially aid in developing algorithms able to predict risk for psychiatric illness and suicidality. Careful consideration of the ethical issues involved in the development and implementation of these methods is needed. Ethical concerns are inherent with the use of risk algorithms for the prediction of illness in general. These concerns are magnified in vulnerable populations, especially in those with mental health disorders or suicidality.

Join via Zoom
Meeting ID: 989 5777 8593

Dial by your location
+1 267 831 0333 US (Philadelphia)
877 853 5247 US Toll-free
Meeting ID: 989 5777 8593 Online via Zoom: https://pitt.zoom.us/j/98957778593 false MM/DD/YYYY 30 OPAQUE aomuBSIqfzhKxWLGPmQv111967

Online via Zoom: https://pitt.zoom.us/j/98957778593

Jody L Stockdill, joc10@pitt.edu

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