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Figure 1
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Team 17

Team Members

Faculty Advisor

Robert McClardy
Jinjian Tong
Yubo Dong
Joshua Redmond
Matthew Gianetta

Dong Shin



sponsored by
Shin Lab

An Electronic Medical Records (EMR) is a collection of digital medical information corresponding to a patient from various clinics. The goal of this project is to integrate clinical data from Epic and dental data from axiUm into an open source EMR management program called Smart EMR. The clients are clinical researchers who will have access to Smart EMR with a key for both axiUm and Epic in order to retrieve information corresponding to different patients. These researchers will be able to access the data within Smart EMR through a user-friendly web User Interface (UI). Additionally, researchers can query the data through regular sentences which are parsed with a natural language parser. The resulting data is examined by an AI model that creates suggestions and different visualizations corresponding to the type of data returned. This way, researchers have an easier time creating inferences and finding associations between diseases and genes that may have been missed otherwise. The data for this project has been managed to meet various constraints and to handle various types. Privacy has to be maintained due to HIPPA compliance rules within the medical field, thus all of the test data is fake. Current biological data sets are based around dental and skeletal features, but our system is extensible and implementation of other biological sets is still possible. Unstructured and structured data, such as a doctor's narrative or lab data respectively, have been considered and handled too through the usage of MySQL and MongoDB. All structured patient information such as lab data and gene mutations are stored within MySQL. All unstructured data types, such as DICOM images or doctor notes, are stored within MongoDB. Finally, all forms on the web pages check for validity in order to remain secure and prevent cyber-attacks.