Assessing socioeconomic bias in machine learning algorithms in health care: a case study of the HOUSES index
YJ Juhn, E Ryu, CI Wi, KS King, M Malik… - Journal of the …, 2022 - academic.oup.com
Journal of the American Medical Informatics Association, 2022•academic.oup.com
Objective Artificial intelligence (AI) models may propagate harmful biases in performance
and hence negatively affect the underserved. We aimed to assess the degree to which data
quality of electronic health records (EHRs) affected by inequities related to low
socioeconomic status (SES), results in differential performance of AI models across SES.
Materials and Methods This study utilized existing machine learning models for predicting
asthma exacerbation in children with asthma. We compared balanced error rate (BER) …
and hence negatively affect the underserved. We aimed to assess the degree to which data
quality of electronic health records (EHRs) affected by inequities related to low
socioeconomic status (SES), results in differential performance of AI models across SES.
Materials and Methods This study utilized existing machine learning models for predicting
asthma exacerbation in children with asthma. We compared balanced error rate (BER) …
Objective
Artificial intelligence (AI) models may propagate harmful biases in performance and hence negatively affect the underserved. We aimed to assess the degree to which data quality of electronic health records (EHRs) affected by inequities related to low socioeconomic status (SES), results in differential performance of AI models across SES.
Materials and Methods
This study utilized existing machine learning models for predicting asthma exacerbation in children with asthma. We compared balanced error rate (BER) against different SES levels measured by HOUsing-based SocioEconomic Status measure (HOUSES) index. As a possible mechanism for differential performance, we also compared incompleteness of EHR information relevant to asthma care by SES.
Results
Asthmatic children with lower SES had larger BER than those with higher SES (eg, ratio = 1.35 for HOUSES Q1 vs Q2–Q4) and had a higher proportion of missing information relevant to asthma care (eg, 41% vs 24% for missing asthma severity and 12% vs 9.8% for undiagnosed asthma despite meeting asthma criteria).
Discussion
Our study suggests that lower SES is associated with worse predictive model performance. It also highlights the potential role of incomplete EHR data in this differential performance and suggests a way to mitigate this bias.
Conclusion
The HOUSES index allows AI researchers to assess bias in predictive model performance by SES. Although our case study was based on a small sample size and a single-site study, the study results highlight a potential strategy for identifying bias by using an innovative SES measure.
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