ECGEL:用于预测心力衰竭的多式12导电图分类模型
Xintong Liang1, Nan Jiang2, Pengjia Qi2
1School of Computer Science and Technology, Zhejiang Sci-Tech University, Hangzhou, 310018 China.
Biomedical engineering letters
|April 24, 2025
概括
这项研究介绍了ECGEL,这是一种用于早期心力衰竭 (HF) 预测的新型多式模式. 通过整合心电图 (ECG) 和临床文本数据,ECGEL 实现了高精度,改善了诊断和患者的治疗结果.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 生物医学信号处理
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因之一.
- 心力衰竭 (HF) 显著降低了患者的生活质量,并产生了巨大的社会经济成本.
- 目前的高频率诊断方法通常是复杂和昂贵的,延迟了关键的早期干预.
研究的目的:
- 为早期心力衰竭预测开发一种高效准确的多式联络模型.
- 通过数据融合解决当前诊断方法的局限性.
- 通过及时干预来改善患者的治疗结果.
主要方法:
- 开发了一种多式模式,ECGEL,集成心电图 (ECG) 和临床文本数据.
- 电脑心电图信号被删除 (LUNet),转换为光谱图,并提取特征 (EfficientNetv2).
- 临床文本数据进行了预处理 (Bert) 和特征提取 (BiLSTM),随后进行特征融合以进行预测.
主要成果:
- 在私人数据集上,ECGEL模型表现出高性能.
- 在心力衰竭预测方面获得了97.9%的准确性,98.3%的召回率和97.6%的F1得分.
- 多式联络方法在提高诊断准确度方面被证明是有效的.
结论:
- ECGEL为早期心力衰竭诊断提供了一种高效准确的解决方案.
- 该模型显示了在心血管疾病管理中临床应用的巨大潜力.
- 整合心电图和临床文本数据可以改善心力衰竭的预测.
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