基于机器学习的集群使用12导电心电图在患有可植入心脏转移器除器的患者中,以识别未来心室节律失常
Ryo Tateishi1,2, Masato Shimizu1, Makoto Suzuki1
1Department of Cardiology, Yokohama Minami Kyosai Hospital.
概括
使用12导电心电图 (ECG) 的机器学习 (ML) 风险分层可以预测可植入心脏转换器-除器 (ICD) 患者的腹腔失律. 这种方法有助于分层治疗可能需要ICD治疗的患者.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 数据科学数据科学数据科学
背景情况:
- 植入式心脏转换器-除器 (ICD) 对于降低心血管高风险心脏病患者的死亡率至关重要.
- 机器学习 (ML) 在心律失常研究中表现有前途,但其用于预测ICD患者心室心律失常的应用尚未探索.
研究的目的:
- 预测和分层需要ICD治疗的心室节律失常.
- 在患有ICD的患者中使用12导电心电图 (ECG) 进行基于ML的风险分层.
主要方法:
- 对200名患有内科疾病的成年患者的回顾性分析.
- 无监督学习技术的应用 (K-means,层次聚类) 和对12导电图数据的维度缩小.
- 用于确定最佳集群方法和集群数量的轮系数.
主要成果:
- 分为3个群集的等级分类显示了最高的准确性 (轮系数=0.585).
- 卡普兰-梅尔分析显示了3个确定集群之间的显著差异 (P=0.026).
- 29.5%的患者在平均2953天的随访期间接受了适当的ICD治疗.
结论:
- 基于ML的12导电心电图的聚类有可能在ICD患者中进行心室失常风险分层.
- 需要进一步的多中心研究来完善ICD的适用性和验证发现.
相关概念视频
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Definition
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