Abstract:Left ventricular ejection fraction (LVEF) assessment depends on echocardiography, limiting access in primary care and resource-constrained settings. We developed a multimodal machine-learning framework that combines engineered 12-lead ECG timeseries features with structured EHR variables to classify LVEF into four clinically used strata: normal (>50%), mildly reduced (40-50%), moderately reduced (30-40%), and severely reduced (<30%). To support model explainability, we identified the most influential ECG and EHR features via SHAP attributions. Using retrospective data from Hartford HealthCare, we trained XGBoost models on 36,784 ECG-echocardiogram pairs from 30,952 outpatients and evaluated temporal generalizability on 19,966 ECGs from a subsequent period. The multimodal model achieved one-vs-rest AUROCs of 0.95 (severe), 0.92 (moderate), 0.82 (mild), and 0.91 (normal), outperforming ECG-only and EHR-only baselines, and maintained performance under temporal validation. This work supports ECG-based, multimodal LVEF stratification as a practical screening and triage aid to prioritize confirmatory imaging where resources are limited.
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2604.25942 [cs.LG] |
| (or arXiv:2604.25942v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2604.25942 arXiv-issued DOI via DataCite |
Submission history
From: Catherine Ning [view email]
[v1]
Fri, 17 Apr 2026 05:21:47 UTC (1,783 KB)