ICMHI 2020

Tutorial Information on 14 August, ICMHI 2020 | Flyer Downloading

Time: 13:00-17:00

Venue: Kamakura Prince Hotel

Registration: 50USD fee for each participant

#Using R software and emr package for electronic health records preprocessing and integration

#Instructor: Prof. Yi-Ju Tseng and Members of Digital Health Lab, Department of Information Management, Chang Gung University, Taiwan.

#Targeted participants:
The main targeted participants of this tutorial are clinical data analysts and other personnel that have a chance to access EHR or clinical data.

#This tutorial is intended to cover the needs and interests of researches and analysts who want to explore the details of EHRs, especially diagnosis and procedure records, and to learn the tricks of clinical data analysis.

Based on the development of electronic health records (EHRs), big data analytics in healthcare is deemed as one of the essential processes that help accelerate the progress of clinical research. Enriched EHRs contain critical information related to disease progression, and access to this information could help in healthcare decision-making, such as treatment selection and disease diagnosis. However, the characteristics of healthcare big data, including heterogeneity and sparseness, make reprocessing and analysis of the information difficult, creating a common bottleneck in healthcare big data analytics.
Preparing a research-ready dataset from EHRs is a complicated and time-consuming task and requires substantial data science skills. We developed an R package - emr – to simplify and accelerate the workflow for EHR data extraction, resulting in simpler and cleaner scripts that are more easily debugged, shared, and reproduced.
This instructional tutorial aims to provides an introduction to mechanisms to analyzing, integrating, and visualizing clinical data, such as diagnosis and procedure records, with R software and an open-source emr package. The emr package helps researchers explore EHRs to acquire crucial information from the data and understand disease progression.
The first part of the tutorial will be devoted to an overview of the diagnosis and procedure data and related standards, presenting the useful resources used in state-of-the-art research papers. The second section will focus on the basic introduction of R software.
In the third and final section, attendees will have the opportunity to acquire hands-on experience (using R with emr package) on how to process electronic health data and generate ready-to-analysis dataset.

#About the lecturers:
Yi-Ju Tseng, PhD
Yi-Ju Tseng is an associate professor at Chang Gung University with extensive experience in claims data and electronic medical records analysis and data mining analytics. Tseng's work focuses on improving infection surveillance by using informatics techniques and applying data mining techniques to infectious disease research. Tseng received the MOST Young Scholar Fellowship and Special Outstanding Talent Award from Ministry of Science and Technology, Taiwan. Her research interests include medical informatics, public health informatics, clinical decision support, claims data analysis, and infection surveillance.

Members of Digital Health Lab
The emr package is mainly developed and utilized by the members of Digital Health Lab, Ms. Hsiang-Ju Chiu, Ms. Chun-Ju Chen, and Ms. Ru-Fang Hu.

#Before join the tutorial:
For the hands-on section, please bring your own devices with R (3.6.1 or later) and RStudio (1.2.5 or later) installed. The installation instructions of emr package will be provided in the tutorial.
R: https://cloud.r-project.org/
RStudio: https://rstudio.com/products/rstudio/download/#download