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使用基于LogNNet的诊断模型识别右心室功能障碍:使用监督的ML算法进行比较研究.

Mehmet Tahir Huyut1, Andrei Velichko2, Maksim Belyaev2

  • 1Department of Biostatistics and Medical Informatics, Faculty of Medicine, Erzincan Binali Yıldırım University, Erzincan, 24000, Turkey. tahir.huyut@erzincan.edu.tr.

Scientific reports
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概括

在急性肺栓塞 (PE) 中,早期发现右心室功能障碍 (RVD) 是至关重要的. LogNNet机器学习模型有效地识别了关键的RVD预测因素,改善了诊断和患者的结果.

关键词:
诊断模型是一种诊断模型.边缘计算是一种边缘计算.功能选择 功能选择在LogNNet上使用LogN.机器学习是机器学习.医疗物联网医疗物联网预测分析是一种预测分析.肺栓塞 肺栓塞 是一种肺栓塞.右心室功能障碍是什么风险评估 风险评估 风险评估血栓形成的原因之一是血栓形成.

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科学领域:

  • 心脏病学 心脏病学
  • 医疗信息学 医疗信息学
  • 机器学习 机器学习

背景情况:

  • 右心室功能障碍 (RVD) 在急性肺栓塞 (PE) 患者中显著增加死亡率.
  • 早期和准确的RVD检测对于及时干预和改善生存率至关重要.
  • 确定急性PE中具有成本效益的RVD风险因素对于临床实践至关重要.

研究的目的:

  • 评估LogNNet和监督机器学习 (ML) 模型,用于诊断急性PE患者的RVD.
  • 使用ML方法识别RVD的显著预测因素.
  • 为实际的RVD诊断提出一个基于集成的LogNNet模型.

主要方法:

  • 用重复分层保留验证来评估模型性能.
  • 使用LogNNet和监督ML模型进行RVD诊断.
  • 进行了特征重要性分析,以确定关键的RVD预测因素.

主要成果:

  • LogNNet确定了性别,冠状动脉疾病,并发性疾病 (高血压),年龄 (>74岁),血栓段和侧面性作为显著的RVD预测因素.
  • 这些特征的组合显示了RVD的高预测能力.
  • 该LogNNet模型表现出强大的性能,具有有限的功能.

结论:

  • LogNNet提供了一个实用且易于使用的工具,用于早期检测PE患者的RVD,即使在资源有限的环境中也是如此.
  • 该模型的效率与很少的功能支持其在边缘设备和临床决策支持系统的使用.
  • 研究结果可以与数字健康创新相结合,以提高患者监测和弹性.