![]() The training materials (codings and books) are very practical. Non-profit staff member at Whitman-Walker Institute, 2021 There were novel techniques introduced that I was not familiar with, and the code review was helpful for understanding how to implement them. This bootcamp was helpful for understanding what kinds of clinical questions can be answered with large-scale EMR data. Instead, the Training will be a live-stream, remote training that takes place over live, online video on June 29-30, 2022 from 10am EDT - ~5pm EDT. Please note this training is not a self-paced, pre-recorded online training. LOCATIONSĬOVID-19 Update: The EMR Boot Camp will not take place in person due to the COVID-19 pandemic. Training scholarships are available for the Electronic Medical Records Boot Camp. She is currently an Associate Editor for the Journal of American Statistical Association (JASA). For her contribution to nonparametric statistics and biostatistics, she received the Noether Young Scholar Award from the American Statistical Association in 2011, and was elected as an American Statistical Association Fellow in 2015. Recently, she has been actively engaged in building analysis tools in bioinformatics and genetic research. She has made several important contributions in developing models for pediatric growth charts and in developing statistical methods to handle measurement errors, missing data, and high-dimensional confounding rising from electronic medical records. ![]() Wei’s work has centered around innovative methods to maximize the potential of large-scale datasets such as electronic medical records. Ying Wei, PhD, Department of Biostatistics, Columbia University. Wang is Professor of Biostatistics in the department of Biostatistics at Mailman School of Public Health. Her research focuses on methodological development in observational studies using electronic health records data and multi-omics data, especially methods for multiple domain fusion or multi-omics integration. Shuang Wang, PhD, Department of Biostatistics, Columbia University. Dr. Personal laptops will be used for multiple boot camp sessions.Įach participant will be required to apply for access to MIMIC-III data, requiring completion of specific HIPAA training to receive credentials. R and R-Studio are available for free download and installation on Mac, PC, and Linux devices. There are four prerequisites and requirements to attend this training:Įach participant must have an introductory background in statistics.Įach participant must be familiar with R.Įach participant must be bring a laptop with latest versions of R and R-Studio downloaded and installed prior to the first day of the workshop. Investigators at all career stages are welcome to attend, and we particularly encourage trainees and early-stage investigators to participate. Statistical methods for predictive analysis Statistical methods for comparative effectiveness ![]() Preparation, transformation and integration of EMR/EHRĬonfounding, bias and missing data in EMR/EHR and statistical methods addressing these challenges 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.īy the end of the electronic medical records training, participants will be familiar with the following topics: They are important data resources for building predictive models for disease diagnosis and prognosis, thus enabling personalized medicine.ĭespite the great potential, analyzing such large, scattered and heterogeneous observational patient data is still technically challenging. EMR/EHRs provide unprecedented opportunities for cohort-wide investigations and knowledge discovery. Extensive effort has been dedicated to developing advanced clinical data processing and data management in order to integrate patient data into a computable collection of rich longitudinal patient profiles. Huge amounts of longitudinal and detailed patient information, including lab tests, medications, disease status, and treatment outcome, have been accumulated and are available electronically. Over the last decade, Electronic Health Records (EHRs) and Electronic Medical Records (EMRs) systems have been increasingly implemented at US hospitals. Electronic medical records TRAINING OVERVIEW
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