Derivation and Validation of a Machine Learning Approach to Detect and Mitigate Biases in Healthcare Data

Abstract

Abstract Background Broad adoption of artificial intelligence (AI) algorithms in healthcare has led to perpetuation of bias found in datasets used for algorithm training. Methods to mitigate bias involve approaches after training leading to tradeoffs between sensitivity and specificity. There have been limited efforts to address bias at the level of the data for algorithm generation. Methods We generate a data-centric, but algorithm-agnostic approach to evaluate dataset bias by investigating how the relationships between different groups are learned at different sample sizes. We name this method AEquity and define a metric AEq. We then apply a systematic analysis of AEq values across subpopulations to identify and mitigate manifestations of racial bias. Findings We demonstrate that AEquity helps mitigate different biases in three different chest radiograph datasets, a healthcare costs dataset, and when using tabularized electronic health record data for mortality prediction. In the healthcare costs dataset, we show that AEquity is a more sensitive metric of label bias than model performance. AEquity can be utilized for label selection when standard fairness metrics fail. In the chest radiographs dataset, we show that AEquity can help optimize dataset selection to mitigate bias, as measured by nine different fairness metrics across nine of the most frequent diagnoses and four different protected categories (race, sex, insurance status, age) and the intersections of race and sex. We benchmark against approaches currently used after algorithm training including recalibration and balanced empirical risk minimization. Finally, we utilize AEquity to characterize and mitigate a previously unreported bias in mortality prediction with the widely used National Health and Nutrition Examination Survey (NHANES) dataset, showing that AEquity outperforms currently used approaches, and is effective at both small and large sample sizes. Interpretation AEquity can identify and mitigate bias in known biased datasets through different strategies and an unreported bias in a widely used dataset. Summary AEquity, a machine learning approach can identify and mitigate bias the level of datasets used to train algorithms. We demonstrate it can mitigate known cases of bias better than existing methods, and detect and mitigate bias that was previously unreported. EVIDENCE IN CONTEXT Evidence before this study Methods to mitigate algorithmic bias typically involve adjustments made after training, leading to a tradeoff between sensitivity and specificity. There have been limited efforts to mitigate bias at the level of the data. Added value of this study This study introduces a machine learning based method, AEquity, which analyzes the learnability of data from subpopulations at different sample sizes, which can then be used to intervene on the larger dataset to mitigate bias. The study demonstrates the detection and mitigation of bias in two scenarios where bias had been previously reported. It also demonstrates the detection and mitigation of bias the widely used National Health and Nutrition Examination Survey (NHANES) dataset, which was previously unknown. Implications of all available evidence AEquity is a complementary approach that can be used early in the algorithm lifecycle to characterize and mitigate bias and thus prevent perpetuation of algorithmic disparities.

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