Fairness gaps in Machine learning models for hospitalization and emergency department visit risk prediction in home healthcare patients with heart failure

摘要: ObjectivesThis study aims to evaluate the fairness performance metrics of Machine Learning (ML) models to predict hospitalization and emergency department (ED) visits in heart failure patients receiving home healthcare. We analyze biases, assess performance disparities, and propose solutions to improve model performance in diverse subpopulations.MethodsThe study used a dataset of 12,189 episodes of home healthcare collected between 2015 and 2017, including structured (e.g., standard assessment tool) and unstructured data (i.e., clinical notes). ML risk prediction models, including Light Gradient-boosting model (LightGBM) and AutoGluon, were developed using demographic information, vital signs, comorbidities, service utilization data, and the area deprivation index (ADI) associated with the patient’s home address. Fairness metrics, such as Equal Opportunity, Predictive Equality, Predictive Parity, and …