Patient Demographic

Patient demographic data is increasingly recognized as a crucial factor influencing the accuracy, fairness, and generalizability of healthcare models, particularly in machine learning applications. Current research focuses on identifying and mitigating biases stemming from demographic variations in datasets used to train diagnostic and predictive models, employing techniques like constrained low-rank approximation for patient clustering and Shapley value analysis to explain performance disparities across sites. This work is vital for ensuring equitable access to high-quality healthcare by improving the robustness and fairness of AI systems and informing more personalized treatment strategies.