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Article

Characterizing advanced heart failure risk and hemodynamic phenotypes using interpretable machine learning.

American heart journal - 1 May 2024

Lamp Josephine, Wu Yuxin, Lamp Steven, Afriyie Prince, Ashur Nicholas, Bilchick Kenneth, Breathett Khadijah, Kwon Younghoon, Li Song, Mehta Nishaki, Pena Edward Rojas, Feng Lu, Mazimba Sula

Abstract excerpt

BACKGROUND: Although previous risk models exist for advanced heart failure with reduced ejection fraction (HFrEF), few integrate invasive hemodynamics or support missing data. This study developed and validated a heart failure (HF) hemodynamic risk and phenotyping score for HFrEF, using Machine Learning (ML). METHODS: Prior to modeling, patients in training and validation HF cohorts were assigned to 1 of 5 risk...

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Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

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Source

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Identifiers

PMID: 38336159
DOI: 10.1016/j.ahj.2024.02.001
PMCID: PMC11042988

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-08.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

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