Predicting High-Risk Patients and High-Risk Outcomes in Heart Failure

Ramsey M. Wehbe, Sadiya S. Khan, Sanjiv J. Shah, Faraz S. Ahmad*

*Corresponding author for this work

Research output: Contribution to journalReview articlepeer-review

2 Scopus citations

Abstract

Identifying patients with heart failure at high risk for poor outcomes is important for patient care, resource allocation, and process improvement. Although numerous risk models exist to predict mortality, hospitalization, and patient-reported health status, they are infrequently used for several reasons, including modest performance, lack of evidence to support routine clinical use, and barriers to implementation. Artificial intelligence has the potential to enhance the performance of risk prediction models, but has its own limitations and remains unproved.

Original languageEnglish (US)
Pages (from-to)387-407
Number of pages21
JournalHeart Failure Clinics
Volume16
Issue number4
DOIs
StatePublished - Oct 2020

Keywords

  • Artificial intelligence
  • Deep learning
  • Heart failure
  • Machine learning
  • Prognosis
  • Risk factors
  • Risk models
  • Risk scores

ASJC Scopus subject areas

  • Cardiology and Cardiovascular Medicine

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