Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods

MLA

Poulain, Raphael, et al. Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods. 2023. EBSCOhost, widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2305.11386&authtype=sso&custid=ns315887.

APA

Poulain, R., Tarek, M. F. B., & Beheshti, R. (2023). Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods.

Chicago

Poulain, Raphael, Mirza Farhan Bin Tarek, and Rahmatollah Beheshti. 2023. “Improving Fairness in AI Models on Electronic Health Records: The Case for Federated Learning Methods.” http://widgets.ebscohost.com/prod/customlink/proxify/proxify.php?count=1&encode=0&proxy=&find_1=&replace_1=&target=https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&scope=site&db=edsarx&AN=edsarx.2305.11386&authtype=sso&custid=ns315887.