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

Award Number: R56HL138415
ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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Interpretable Deep Learning Model for Longitudinal Electronic Health Records and Applications to Heart Failure Prediction - PROJECT SUMMARY Heart failure (HF) is a highly disabling and costly disease with a high mortality rate. In the prediagnostic phase (i.e., 1236 months before diagnosis), HF is difficult to detect given the insidious signs and symptoms. After diagnosis, where it is not possible to reverse disease progression, efforts are made to avoid hospital admission and readmission, but with limited capabilities to stratify patients by risk. We propose to develop interpretable deep learning models applied to largescale electronic health record (EHR) data to detect HF related events on two different time scales. One set of models will be developed to detect HF diagnosis one to two years before actual documented diagnosis. Separately, we propose to identify HF patients who are at risk of hospital admission and readmission . The project focuses on developing deep learning models that offer the potential for greater accuracy, clinical interpretability, and utility than alternatives. The expected deliverables include comprehensive software for creating deep learning algorithms that predict HF outcomes and related software tools for model visualization.


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2023 ( Subtotal = -$50,650 )
2023	2017	GEORGIA TECH RESEARCH CORPORATION	926 DALNEY ST NW	ATLANTA	GA	30332	FULTON	USA	Cardiovascular Diseases Research	000	1	10/1/2022	NEW	-$50,650
														Subtotal = -$50,650

Issue Date FY: 2021 ( Subtotal = -$24,237 )
2021	2017	GEORGIA TECH RESEARCH CORPORATION	926 DALNEY ST NW	ATLANTA	GA	30332	FULTON	USA	Cardiovascular Diseases Research	000	1	3/9/2021	NEW	-$24,237
														Subtotal = -$24,237

Issue Date FY: 2020 ( Subtotal = $0 )
2020	2017	GEORGIA TECH RESEARCH CORPORATION	926 DALNEY ST NW	ATLANTA	GA	30318	FULTON	USA	Cardiovascular Diseases Research	000	1	7/15/2020	NEW	$0
														Subtotal = $0

Issue Date FY: 2019 ( Subtotal = $0 )
2019	2017	GEORGIA TECH RESEARCH CORPORATION	505 10TH ST NW	ATLANTA	GA	30318	FULTON	USA	Cardiovascular Diseases Research	000	1	8/13/2019	NEW	$0
														Subtotal = $0

Issue Date FY: 2017 ( Subtotal = $756,093 )
2017	2017	GEORGIA TECH RESEARCH CORPORATION	505 10TH ST NW	ATLANTA	GA	30318	FULTON	USA	Cardiovascular Diseases Research	000	1	9/13/2017	NEW	$756,093
														Subtotal = $756,093

Grand Total All Awards = $681,206

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

Award Number: R56HL138415

ORGANIZATION: NATIONAL HEART, LUNG, & BLOOD INSTITUTE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

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