Tackling Health Inequity using Machine Learning Fairness, AI, and Optimization

Miao will discuss the tools and techniques used to assess, visualize, and improve equity in clinical trials. A set of novel equity metrics for clinical trials is constructed from Machine Learning (ML) Fairness Research to quantify inequities of various subgroups defined over multiple demographic or clinical characteristics, such as Hispanic female subjects who are underweight or no-Hispanic black male subjects aged over 64 and with high fasting glucose level. A tool called TrialEquity, which is developed based on the proposed equity metrics, is designed to provide insights to improve the clinical trial equity and health equity, with specific considerations for diverse user groups including clinicians, researchers, and health policy advocates. The tool is able to design new equitable  clinical trials, evaluate ongoing/conducted studies, provide remedial advice for inequitable trials,  accommodate how evidence from trials applies to the individual needs of patients, and guide equitable decisions for users.