Summer 2023 dates: Livestream, online training August 21-22, 2023; 10:00am - ~5:30pm ET.
The Electronic Medical Records Boot Camp is a two-day intensive boot camp of seminars and hands-on analytical sessions to provide an overview of electronic health data opportunities, statistical challenges, and latest techniques.
Despite the great potential, analyzing such large, scattered and heterogeneous observational patient data is still technically challenging. This two-day intensive workshop will go over opportunities and potentials of EMR/EHR for health and medical studies, statistical challenges and pitfalls for analyzing EMR/EHR, and the latest developments of multiple techniques to address those challenges, followed by hands-on computer lab sessions and case studies to put concepts into practice.
By the end of the electronic medical records training, participants will be familiar with the following topics:
- Power and potentials of EMR/EHR data
- Open-access datasets across the world
- Preparation, transformation and integration of EMR/EHR
- Confounding, bias and missing data in EMR/EHR and statistical methods addressing these challenges
- Statistical methods for comparative effectiveness
- Statistical methods for predictive analysis
AUDIENCE AND REQUIREMENTS
Investigators from any institution and from all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. There are four requirements to attend this training:
- Each participant must have an introductory background in statistics.
- Each participant must be familiar with R.
- Each participant must have a laptop/computer with latest versions of R and R-Studio downloaded and installed prior to the first day of the workshop. R and R-Studio are available for free download and installation on Mac, PC, and Linux devices.
- Each participant will be required to apply for access to MIMIC-III data, requiring completion of specific HIPAA training to receive credentials.
INSTRUCTORS
Shuang Wang, PhD, Department of Biostatistics, Columbia University.
Ying Wei, PhD, Department of Biostatistics, Columbia University.
ADDITIONAL INFORMATION
Capacity is limited. Paid registration is required to attend.
