Bias Assessment and Correction in Machine Learning Algorithms: A Use-Case in a Natural Language Processing Algorithm to Identify Hospitalized Patients with Unhealthy Alcohol Use.

Abstract

Unhealthy alcohol use represents a major economic burden and cause of morbidity and mortality in the United States. Implementation of interventions for unhealthy alcohol use depends on the availability and accuracy of screening tools. Our group previously applied methods in natural language processing and machine learning to build a classifier for unhealthy alcohol use. In this study, we sought to evaluate and address bias through the use-case of our classifier. We demonstrated the presence of biased unhealthy alcohol use risk underestimation among Hispanic compared to Non-Hispanic White trauma inpatients, 18- to 44-year-old compared to 45 years and older medical/surgical inpatients, and Non-Hispanic Black compared to Non-Hispanic White medical/surgical inpatients. We further showed that intercept, slope, and concurrent intercept and slope recalibration resulted in minimal or no improvements in bias-indicating metrics within these subgroups. Our results exemplify the importance of integrating bias assessment early into the classifier development pipeline.