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GW: Researchers Aim to Create Machine-Learning Method to Better Understand Racial Disparities in Health Care

George Washington University Milken Institute School of Public Health (Milken Institute SPH) has received a $1 million grant from the National Institute on Minority Health and Health Disparities to use machine-learning techniques to create an innovative way to better understand racial disparities associated with common surgical and medical procedures.

Dr. Yan Ma, an associate professor and vice chair of the Department of Biostatistics and Bioinformatics at Milken Institute SPH, will serve as the principal investigator of the four-year project.

The researchers knew that the large national databases currently used to study racial disparities in health care typically were missing key data, such as information on the patient’s ethnic group or race. As demonstrated in their prior work, missing race has implications for the accuracy of other patient characteristics such as older age, length of stay, emergency admissions, anesthesia type and payer. To address this problem, the team plans to use novel statistical methods based on machine-learning techniques that can impute the missing data.

Once the team has created more precise data sources, they will use them to assess racial disparities such as having a higher risk of dying or complications after undergoing total joint arthroplasty, one of the most common surgical procedures in the United States.

“Our goal is to build a powerful, more accurate way of illuminating the racial disparities in procedures like arthroplasty, including knee or hip replacements,” Dr. Ma said. “Ultimately, we hope our machine-learning method will improve the quality of research on all kinds of health care disparities.

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