Predicting need for heart failure advanced therapies using an interpretable tropical geometry-based fuzzy neural network.

ABSTRACT:

Background

Timely referral for advanced therapies (i.e., heart transplantation, left ventricular assist device) is critical for ensuring optimal outcomes for heart failure patients. Using electronic health records, our goal was to use data from a single hospitalization to develop an interpretable clinical decision-making system for predicting the need for advanced therapies at the subsequent hospitalization.

Methods

Michigan Medicine heart failure patients from 2013-2021 with a left ventricular ejection fraction ≤ 35% and at least two heart failure hospitalizations within one year were used to train an interpretable machine learning model constructed using fuzzy logic and tropical geometry. Clinical knowledge was used to initialize the model. The performance and robustness o

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10684094 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature