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Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups
journal contributionposted on 15.11.2022, 21:51 authored by Hale M Thompson, Brihat Sharma, Sameer Bhalla, Randy Boley, Connor McCluskey, Dmitriy Dligach, Matthew M Churpek, Niranjan S Karnik, Majid Afshar
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.
Great Lakes Node of the Drug Abuse Clinical Trials Network | Funder: National Institutes of Health (National Institute on Drug Abuse) | Grant ID: UG1DA049467
Employing eSBI in a Community-based HIV Testing Environment for At-risk Youth | Funder: National Institute on Drug Abuse | Grant ID: R01DA041071
CitationThompson, H. M., Sharma, B., Bhalla, S., Boley, R., McCluskey, C., Dligach, D., Churpek, M. M., Karnik, N. S.Afshar, M. (2021). Bias and fairness assessment of a natural language processing opioid misuse classifier: detection and mitigation of electronic health record data disadvantages across racial subgroups. Journal of the American Medical Informatics Association, 28(11), ocab148--. https://doi.org/10.1093/jamia/ocab148
PublisherOxford University Press (OUP)
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Substance AbusePrevention3 Good Health and Well Beingstructural racismbias and fairnessmachine learningnatural language processingopioid use disorderinterpretabilityElectronic Health RecordsHispanic or LatinoHumansMachine LearningNatural Language ProcessingOpioid-Related DisordersMedical InformaticsInformation and Computing SciencesEngineeringMedical and Health Sciences