Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups

OBJECTIVES: To assess fairness and bias of a previously validated machine learning opioid misuse classifier. MATERIALS & METHODS: Two experiments were conducted with the classifier’s original (n = 1000) and external validation (n = 53 974) datasets from 2 health systems. Bias was assessed via te...

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Detalles Bibliográficos
Autores principales: Thompson, Hale M, Sharma, Brihat, Bhalla, Sameer, Boley, Randy, McCluskey, Connor, Dligach, Dmitriy, Churpek, Matthew M, Karnik, Niranjan S, Afshar, Majid
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2021
Materias:
Research and Applications
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510285/
https://www.ncbi.nlm.nih.gov/pubmed/34383925
http://dx.doi.org/10.1093/jamia/ocab148

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8510285/
https://www.ncbi.nlm.nih.gov/pubmed/34383925
http://dx.doi.org/10.1093/jamia/ocab148

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