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Interpretable Deep Learning Model for Longitudinal Electronic Health Record and Applications to Heart Failure Prediction

Description

Thisproject is to develop advanced deep learning model to predict early onset ofheart failure using electronic health records, including structured (e.g.,diagnosis, medication order, etc.) and unstructured (i.e., progress notes, imagereports, etc.)data.

PI is Jimeng Sun, Ph.D., School of Computational Sciences and Engineering at College of Computing, Georgia Institute of Technology. Site PI is Xiaowei Yan, Ph.D.

Principal Investigator

External PI, Principal Investigator, Xiaowei (Sherry) Yan, PhD, MS

Start Date

November 01, 2017

End Date

October 30, 2019

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Create a longitudinal proof-of-concept MS research database (MSClinQ) that can support clinical queries, and can be updated with new tx, dx tools, care options

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