Affiliate, Primary Care and Population Health
https://profiles.stanford.edu/juan-m-banda
Dr. Juan M. Banda, PhD is a computer scientist and a SAGE Center awardee. His research focuses on machine learning, computer vision, and Natural Language Processing methods that help to generate insights from multi-modal large-scale data sources. With applications to precision medicine, medical informatics, astroinformatics as well as other domains, working with large volumes of image data, extracting and transforming computer vision image features into large content-based image retrieval systems for NASA’s Solar Dynamics Observatory mission. He is also well-versed in extracting terms and clinical concepts from millions of unstructured electronic health records and using them to build predictive models (electronic phenotyping) and mine for potential multi-drug interactions (drug safety). His work in electronic phenotyping includes leading the development of APHRODITE, a tool that allows researchers to build phenotypes using noisy labels. Dr. Banda has published over 45 peer-reviewed conference and journal papers. He is an active collaborator of the Observational Health Data Sciences and Informatics and his work has been funded by the Department of Veteran Affairs, National Institute of Aging as well as NASA, NSF, and NIH.
SAGE Project: Are phenotyping algorithms fair for underrepresented minorities within older adults?
Dr. Banda’s SAGE project is focused on the following question: “Are phenotyping algorithms fair for underrepresented minorities within older adults?” Through this project, he proposes to determine the presence of racial and age bias in probabilistic phenotype algorithms in both local, and national datasets. After characterizing bias, he will develop best-practices and software tools to improve the phenotyping process to achieve fairness for underrepresented minorities.
