Conceptualizing bias in EHR data: A case study in performance disparities by demographic subgroups for a pediatric obesity incidence classifier

Abstract

AbstractElectronic Health Records (EHRs) are increasingly used to develop machine learning models in predictive medicine. There has been limited research on utilizing machine learning methods to predict childhood obesity and related disparities in classifier performance among vulnerable patient subpopulations. In this work, classification models are developed to recognize pediatric obesity using temporal condition patterns obtained from patient EHR data. We trained four machine learning algorithms (Logistic Regression, Random Forest, XGBoost, and Neural Networks) to classify cases and controls as obesity positive or negative, and optimized hyperparameter settings through a bootstrapping methodology. To assess the classifiers for bias, we studied model performance by population subgroups then used permutation analysis to identify the most predictive features for each model and the demographic characteristics of patients with these features. Mean AUC-ROC values were consistent across classifiers, ranging from 0.72-0.80. Some evidence of bias was identified, although this was through the models performing better for minority subgroups (African Americans and patients enrolled in Medicaid). Permutation analysis revealed that patients from vulnerable population subgroups were over-represented among patients with the most predictive diagnostic patterns. We hypothesize that our models performed better on under-represented groups because the features more strongly associated with obesity were more commonly observed among minority patients. These findings highlight the complex ways that bias may arise in machine learning models and can be incorporated into future research to develop a thorough analytical approach to identify and mitigate bias that may arise from features and within EHR datasets when developing more equitable models.Author SummaryChildhood obesity is a pressing health issue. Machine learning methods are useful tools to study and predict the condition. Electronic Health Record (EHR) data may be used in clinical research to develop solutions and improve outcomes for pressing health issues such as pediatric obesity. However, EHR data may contain biases that impact how machine learning models perform for marginalized patient subgroups. In this paper, we present a comprehensive framework of how bias may be present within EHR data and external sources of bias in the model development process. Our pediatric obesity case study describes a detailed exploration of a real-world machine learning model to contextualize how concepts related to EHR data and machine learning model bias occur in an applied setting. We describe how we evaluated our models for bias, and considered how these results are representative of health disparity issues related to pediatric obesity. Our paper adds to the limited body of literature on the use of machine learning methods to study pediatric obesity and investigates the potential pitfalls in using a machine learning approach when studying social significant health issues.