The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation

The Use of Synthetic Electronic Health Record Data and Deep Learning to Improve Timing of High-Risk Heart Failure Surgical Intervention by Predicting Proximity to Catastrophic Decompensation

Objective: Although many clinical metrics are associated with proximity to decompensation in heart failure (HF), none are individually accurate enough to risk-stratify HF patients on a patient-by-patient basis. The dire consequences of this inaccuracy in risk stratification have profoundly lowered t...

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Bibliographic Details
Main Authors: Aixia Guo, Randi E. Foraker, Robert M. MacGregor, Faraz M. Masood, Brian P. Cupps, Michael K. Pasque
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-12-01
Series:Frontiers in Digital Health
Subjects:
electronic health record (EHR)
machine/deep learning
heart failure
synthetic data
surgical intervention
Online Access:https://www.frontiersin.org/articles/10.3389/fdgth.2020.576945/full

Internet

https://www.frontiersin.org/articles/10.3389/fdgth.2020.576945/full

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