Assessing fairness in machine learning models: A study of racial bias using matched counterparts in mortality prediction for patients with chronic diseases
This study contributes to research on fairness assessment by focusing on the examination of systematic disparities and underscores the potential for revealing racial bias in machine learning models used in clinical settings.
Jun 11, 2024
A machine learning approach to robustly determine director fields and analyze defects in active nematics
We developed a machine learning model for extracting reliable director fields from raw experimental images, which enables accurate analysis of topological defects.
Jan 31, 2024