Upcoming Webinar on Data and Modeling Ethics
Scheduled
You’re invited to “One-model-predicts-all No More: Training Specialized Models for Minority Patient Groups” webinar with speaker Dr. Daphne Yao
Date: Friday, March 24, 2023
Time: 12:00 PM – 1:00 PM EST
To register: https://virginiatech.zoom.us/meeting/register/tZcscuGqqD0jHtcgPzkLbaktVvmHa_0M9oh7
Abstract:
Clinical datasets contain information about patients of different races and ages. Some groups of patients may be larger in size than others. For example, some clinical datasets contain many more white patients, which form the majority group, than Black patients, a minority group. Prediction models built on these imbalanced clinical data may provide inaccurate predictions for the minority patients. I will present our recent work on improving the prediction accuracy for minority patients in important medical applications, such as estimating the likelihood of a patient dying in an emergency room visit or surviving cancer. We have designed a bias correction technique that builds customized prediction models for different demographic groups. Our results reveal that subpopulation-specific models show better performance for minority groups. I will also discuss other important problems in achieving equitable digital health in U.S.
Speaker bio:
Dr. Danfeng (Daphne) Yao is a Professor of Computer Science at Virginia Tech. She is an Elizabeth and James E. Turner Jr. '56 Faculty Fellow and CACI Faculty Fellow. Her research interests include building cyber defenses, as well as machine learning for digital health, with a shared focus on accuracy and deployment. She creates new models, algorithms, techniques, and deployment-quality tools for securing large-scale software and systems. Her patents on anomaly detection are extremely influential in the industry, cited by patents from major cybersecurity firms and technology companies. Dr. Yao is an IEEE Fellow for her contributions to enterprise data security and high-precision vulnerability screening. In 2021, she received the prestigious ACM CODASPY Lasting Research Award. She is also an ACM Distinguished Scientist. Previously, she received the NSF CAREER Award and ARO Young Investigator Award. Daphne received her Ph.D. degree from Brown University (Computer Science), M.S. degrees from Princeton University (Chemistry) and Indiana University (Computer Science), Bloomington, B.S. degree from Peking University in China (Chemistry).
Date: Friday, March 24, 2023
Time: 12:00 PM – 1:00 PM EST
To register: https://virginiatech.zoom.us/meeting/register/tZcscuGqqD0jHtcgPzkLbaktVvmHa_0M9oh7
Abstract:
Clinical datasets contain information about patients of different races and ages. Some groups of patients may be larger in size than others. For example, some clinical datasets contain many more white patients, which form the majority group, than Black patients, a minority group. Prediction models built on these imbalanced clinical data may provide inaccurate predictions for the minority patients. I will present our recent work on improving the prediction accuracy for minority patients in important medical applications, such as estimating the likelihood of a patient dying in an emergency room visit or surviving cancer. We have designed a bias correction technique that builds customized prediction models for different demographic groups. Our results reveal that subpopulation-specific models show better performance for minority groups. I will also discuss other important problems in achieving equitable digital health in U.S.
Speaker bio:
Dr. Danfeng (Daphne) Yao is a Professor of Computer Science at Virginia Tech. She is an Elizabeth and James E. Turner Jr. '56 Faculty Fellow and CACI Faculty Fellow. Her research interests include building cyber defenses, as well as machine learning for digital health, with a shared focus on accuracy and deployment. She creates new models, algorithms, techniques, and deployment-quality tools for securing large-scale software and systems. Her patents on anomaly detection are extremely influential in the industry, cited by patents from major cybersecurity firms and technology companies. Dr. Yao is an IEEE Fellow for her contributions to enterprise data security and high-precision vulnerability screening. In 2021, she received the prestigious ACM CODASPY Lasting Research Award. She is also an ACM Distinguished Scientist. Previously, she received the NSF CAREER Award and ARO Young Investigator Award. Daphne received her Ph.D. degree from Brown University (Computer Science), M.S. degrees from Princeton University (Chemistry) and Indiana University (Computer Science), Bloomington, B.S. degree from Peking University in China (Chemistry).
News Chemical and Process Industries Division
03/05/2023 6:00am CST
