Dr. Xiaoqian Jiang: Sensitive Data Detection with High-Throughput Machine Learning Models in Electronic Health Records

Dr. Jiang explored how a groundbreaking discovery was utilized to generate 30 metadata-based features through machine learning for the automatic detection of PHI fields in structured Electronic Health Record (EHR) data. […]

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Dr. Bradley Malin: One Size Does Not Fit All: How to Build Respectful Cohorts for Biomedical Data Science

Dr. Malin drew upon examples from large-scale data-driven projects like the EMR and bio-repository at Vanderbilt University Medical Center, the eMERGE consortium of the NIH, and the All of Us Research Program, aiming to create a comprehensive database of EMRs, genome sequences, and mHealth records from one million Americans. […]

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Dr. Debra Mathews: Using Population Descriptors in Genetics and Genomics Research: A New Framework for an Evolving Field — Lessons from a NASEM Consensus Study for AI-READI

Dr Mathews provided an overview of the recent NASEM consensus study report that focused on the use of race and ethnicity and other population descriptors in genomics research, including the recommendations made by the committee. […]

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