Heartfailure(HF)isahighlydisablingandcostlydiseasewithahighmortalityrate.Intheprediagnosticphase (i.e., 1236 months before diagnosis), HF is difficult to detect given the insidious signs and symptoms. After diagnosis, where it isnotpossibletoreversediseaseprogression,effortsaremadetoavoidhospitaladmission and readmission, but with limited capabilities to stratify patients by risk. We propose to develop interpretable deeplearningmodelsappliedtolargescaleelectronichealthrecord(EHR)datatodetectHFrelatedeventson two different time scales. One set of modelswillbedevelopedtodetectHFdiagnosisonetotwoyearsbefore actual documented diagnosis. Separately, we propose to identify HF patients who are at risk of hospital admission and readmission.Theprojectfocusesondevelopingdeeplearningmodelsthatofferthepotentialfor 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 toolsformodelvisualization.

Public Health Relevance

Deep learning has shown tremendous success in many domains but is yet to have similar impact in health care. The key challenges in healthcare applications are the lack of interpretation for deep learning models and limited transferability of the models across institutions. We develop interpretable deep learning algorithms for heart failure prediction that can handle large longitudinal patient records and are able to adapt across institutions.

Agency
National Institute of Health (NIH)
Institute
National Heart, Lung, and Blood Institute (NHLBI)
Type
High Priority, Short Term Project Award (R56)
Project #
1R56HL138415-01
Application #
9544376
Study Section
Biomedical Computing and Health Informatics Study Section (BCHI)
Program Officer
Coady, Sean
Project Start
2017-09-13
Project End
2019-08-31
Budget Start
2017-09-13
Budget End
2019-08-31
Support Year
1
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Georgia Institute of Technology
Department
Physics
Type
Schools of Arts and Sciences
DUNS #
097394084
City
Atlanta
State
GA
Country
United States
Zip Code
30318