Speakers: Mei Liu, PhD
Mei Liu, PhD
Associate Professor and Interim Director
Division of Medical Informatics, Department of Internal Medicine
University of Kansas Medical Center
Dr. Mei Liu is Associate Professor and interim Director of Medical Informatics at the University of Kansas Medical Center. She received her PhD in Computer Science from the University of Kansas and completed her National Library of Medicine postdoctoral fellowship in Biomedical Informatics at Vanderbilt University. Her research interest is in the development of novel machine learning and artificial intelligence techniques to accelerate risk factor identification and discovery in medicine using electronic medical records (EMR). She is the principal investigator for an NIH R01 and an NSF Smart and Connected Health project that utilizes EMR data from 11 institutions to build machine learning models for Acute Kidney Injury (AKI) prediction and risk factor identification. She is also co-leading the Informatics Core of the Frontiers – Clinical Translational Science Institute at the University of Kansas.
Life Cycle of EHR Data for Clinical Research
Routinely collected patient electronic health record (EHR) data are approaching the genomic scale in volume and complexity and is increasingly recognized as a valuable resource for clinical research to answer questions for broader populations than would have never been possible with a specialized research environment. In general, for clinical research, the EHR data life cycle involves multiple phases: data accrual from the original source data, data curation to form clinical data repository, data transformation and enrichment to create data warehouse, and data extraction for study specific analyses. Dr. Liu will discuss how EHR data can be standardized, transformed, and enriched with linkages to external data to better support translational research needs.