Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

Enhancing heart failure treatment decisions: interpretable machine learning models for advanced therapy eligibility prediction using EHR data

Abstract Timely and accurate referral of end-stage heart failure patients for advanced therapies, including heart transplants and mechanical circulatory support, plays an important role in improving patient outcomes and saving costs. However, the decision-making process is complex, nuanced, and time...

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Bibliographic Details
Main Authors: Yufeng Zhang, Jessica R. Golbus, Emily Wittrup, Keith D. Aaronson, Kayvan Najarian
Format: Article
Language:English
Published: BMC 2024-02-01
Series:BMC Medical Informatics and Decision Making
Subjects:
Heart failure
Electronic health records
Machine learning
Interpretability
Online Access:https://doi.org/10.1186/s12911-024-02453-y

Internet

https://doi.org/10.1186/s12911-024-02453-y

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