Affiliation:
1. Department of Psychiatry & Behavioral Sciences, Rush University Medical Center, Chicago, Illinois, USA
2. Department of Computer Science, Loyola University, Chicago, Illinois, USA
3. Department of Medicine, University of Wisconsin, Madison, Wisconsin, USA
Abstract
Abstract
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 testing for differences in type II error rates across racial/ethnic subgroups (Black, Hispanic/Latinx, White, Other) using bootstrapped 95% confidence intervals. A local surrogate model was estimated to interpret the classifier’s predictions by race and averaged globally from the datasets. Subgroup analyses and post-hoc recalibrations were conducted to attempt to mitigate biased metrics.
Results
We identified bias in the false negative rate (FNR = 0.32) of the Black subgroup compared to the FNR (0.17) of the White subgroup. Top features included “heroin” and “substance abuse” across subgroups. Post-hoc recalibrations eliminated bias in FNR with minimal changes in other subgroup error metrics. The Black FNR subgroup had higher risk scores for readmission and mortality than the White FNR subgroup, and a higher mortality risk score than the Black true positive subgroup (P < .05).
Discussion
The Black FNR subgroup had the greatest severity of disease and risk for poor outcomes. Similar features were present between subgroups for predicting opioid misuse, but inequities were present. Post-hoc mitigation techniques mitigated bias in type II error rate without creating substantial type I error rates. From model design through deployment, bias and data disadvantages should be systematically addressed.
Conclusion
Standardized, transparent bias assessments are needed to improve trustworthiness in clinical machine learning models.
Funder
Agency for Healthcare Research & Quality
National Institute for Drug Abuse
National Institute of Drug Abuse
National Institute on Alcohol Abuse and Alcoholism
National Institute on Drug Abuse
National Center for Advancing Translational Sciences
Dr. Matthew M. Churpeck declares patent pending
National Institute of General Medical Sciences
National Library of Medicine
Publisher
Oxford University Press (OUP)
Cited by
43 articles.
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