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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

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

Start Date

November 01, 2017

End Date

October 30, 2019

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Quantifying 2D and 3D MRI Imaging For Improving MS Diagnosis, Treatment, And Clinical Care

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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Purpose is to provide descriptive info for improving health outcomes by identifying a subgroup of high-frequency ER patients who may have severe mental illness.

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