arXiv 2604.259422026-04-17cs.LG

一种多模态且可解释的机器学习方法用于从心电图中诊断多类射血分数

A Multimodal and Explainable Machine Learning Approach to Diagnosing Multi-Class Ejection Fraction from Electrocardiograms

Operations Research Center, Massachusetts Institute of Technology（麻省理工学院运营研究中心）；Wisconsin School of Business, University of Wisconsin–Madison（威斯康星大学麦迪逊分校商学院）；Heart and Vascular Institute, Hartford Hospital（哈特福德医院心脏与血管研究院）

Catherine Ning, Yu Ma, Cindy Beini Wang, Sean McMahon, Joseph Radojevic, Steven Zweibel, Dimitris Bertsimas

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

本文提出一种结合工程化12导联ECG时间序列特征与结构化电子健康记录变量的多模态机器学习框架，用于四类临床射血分数分类，并通过SHAP属性分析提高模型可解释性。

摘要：

左心室射血分数（LVEF）评估依赖超声心动图，限制了在初级医疗和资源匮乏环境中的应用。我们开发了一种多模态机器学习框架，结合工程化12导联ECG时间序列特征和结构化EHR变量，将LVEF分为四个临床使用分层：正常（>50%）、轻度降低（40-50%）、中度降低（30-40%）和严重降低（<30%）。为支持模型可解释性，我们通过SHAP属性确定了最影响ECG和EHR特征。使用哈特福健康医疗的回顾性数据，我们在30,952名门诊患者中训练XGBoost模型，利用36,784个ECG-超声心动图配对数据，并在后续时期19,966个ECG上评估时间泛化性。多模态模型在单对多AUCs上达到0.95（严重）、0.92（中度）、0.82（轻度）和0.91（正常），优于仅ECG和仅EHR基线，并在时间验证中保持性能。本工作支持基于ECG的多模态LVEF分层作为实际筛查和分诊工具，以优先考虑资源有限情况下确认性影像检查。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。