机器学习在预防心力衰竭中的作用
Arsalan Hamid1, Matthew W Segar2, Biykem Bozkurt3,4
1Division of Cardiology, Department of Medicine, Baylor College of Medicine, 6655 Travis Street, Suite 320, Houston, TX, 77030, USA. arsalan93@hotmail.com.
Heart failure reviews
|October 7, 2024
概括
机器学习 (ML) 可以通过完善风险预测和识别早期疾病,显著改善心力衰竭 (HF) 的预防. 这项技术提供了新的策略来应对日益增长的全球高频负担.
科学领域:
- 心脏病学 心脏病学
- 生物医学工程 生物医学工程
- 人工智能在医学中的应用
背景情况:
- 心力衰竭 (HF) 构成了日益严重的全球健康挑战,特别是在预防症状性疾病 (C期) 方面.
- 在目前的模型中,早期检测心力衰竭前期 (B阶段) 很难,这限制了有效的干预.
- 通过保留喷射分数来预防HF仍然是一个重大未满足的需求.
研究的目的:
- 审查机器学习 (ML) 在预防心力衰竭方面的应用.
- 探索ML在克服HF前检测和干预方面的挑战的潜力.
- 讨论ML在HF预防中的好处,陷和未来方向.
主要方法:
- 审查关于ML在心血管疾病预防中的应用现有文献.
- 分析用于风险预测,诊断信号捕获 (心电图,X射线,心声图) 和生物标志物解释的ML策略.
- 讨论ML在识别有风险的个体 (A阶段) 和患有HF前的个体 (B阶段) 中的作用.
主要成果:
- 机器学习可以增强高频风险预测模型,改善风险人群的早期识别.
- ML算法可以从各种诊断测试中检测出微妙的结构/功能心脏异常.
- ML有助于解释复杂的生物标志物和表观遗传数据,以评估HF风险.
结论:
- 机器学习提供了强大的工具来扩大HF查和识别HF前人口.
- 通过ML可以进行早期干预,以防止症状性心力衰竭 (C阶段) 的进展.
- 尽管有局限性,但ML在HF预防中的好处超过了风险,为未来的进步铺平了道路.
相关概念视频
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