Data from Electronic Health Records (EHRs) are a rich source of patient centered data, including information on the patient’s clinical condition, laboratory results, diagnoses, and treatments. However, access and utilization of EHR data for research presents specific challenges. In this presentation, specific data format obtained from TriNetX and Honest Broker will be illustrated. Critical procedures for data wrangling will be demonstrated using EHR to transform and map data from one raw data form into another format so that the data become more appropriate and valuable for downstream purposes such as data visualization and statistical analysis.
About the Speaker:
Dr. Huang is a Professor and Program lead of Biostatistics at the Zilber School of Public Health, University of Wisconsin, Milwaukee. Dr. Huang has extensive experience in omics data analysis and has been involved in several large cardiovascular cohort studies. Dr. Huang collaborates with biomedical researchers who conduct basic science, clinical trials, and epidemiology studies and provides statistical consultation and analysis support for translational research.
NIH Funding Acknowledgment:Important Reminder – Please acknowledge the NIH when publishing papers, patents, projects, and presentations resulting from the use of CTSI resources by including the NIH Funding Acknowledgement.
