Auditing ICU Readmission Rates in an Clinical Database: An Analysis of Risk Factors and Clinical Outcomes
This work addresses fairness and bias issues in AI systems for healthcare, specifically for patients and clinicians, but it is incremental as it applies existing fairness audit methods to a new dataset.
The study tackled the problem of auditing ICU readmission rates in a clinical database, using machine learning models to classify 30-day readmissions and conducting a fairness audit that uncovered disparities in performance across subgroups based on sensitive attributes like gender and ethnicity.
This study presents a machine learning (ML) pipeline for clinical data classification in the context of a 30-day readmission problem, along with a fairness audit on subgroups based on sensitive attributes. A range of ML models are used for classification and the fairness audit is conducted on the model predictions. The fairness audit uncovers disparities in equal opportunity, predictive parity, false positive rate parity, and false negative rate parity criteria on the MIMIC III dataset based on attributes such as gender, ethnicity, language, and insurance group. The results identify disparities in the model's performance across different groups and highlights the need for better fairness and bias mitigation strategies. The study suggests the need for collaborative efforts among researchers, policymakers, and practitioners to address bias and fairness in artificial intelligence (AI) systems.