机器学习驱动的表型化在心力衰竭中与保存的射出分数:当前的方法和未来的方向
Victoria Potoupni1, Athanasios Samaras2, Christodoulos Papadopoulos1
1Third Department of Cardiology, Ippokratio General Hospital, Aristotle University of Thessaloniki, 546 42 Thessaloniki, Greece.
Medicina (Kaunas, Lithuania)
|November 27, 2025
概括
机器学习 (ML) 通过分析各种患者数据,有助于识别心力衰竭与保存喷射率 (HFpEF) 亚组. 这种方法改善了诊断,风险分层和个性化治疗,以获得更好的患者结果.
科学领域:
- 心脏病学 心脏病学
- 生物医学信息学 生物医学信息学
- 人工智能的人工智能
背景情况:
- 由于心力衰竭的多样性和很少的治疗选择,心力衰竭与保存的喷射分数 (HFpEF) 构成了重大的临床挑战.
- 有效的患者表型确定对于提高HFpEF诊断,预后和个性化治疗策略至关重要.
研究的目的:
- 探索机器学习 (ML) 在识别不同的HFpEF子组中的应用.
- 展示ML如何整合多方面的数据以改善患者分层和管理.
主要方法:
- 利用机器学习模型分析综合数据集,包括临床,成像,生物标志物和生理参数.
- 开发了算法来发现HFpEF患者数据中的复杂模式.
主要成果:
- ML模型成功地确定了临床相关的HFpEF子组,揭示了传统方法无法发现的模式.
- 该方法显示了增强风险分层和个性化治疗指导的潜力.
结论:
- 机器学习提供了一种强大的方法来改进HFpEF表型,从而提高诊断准确性和治疗个性化.
- 人工智能,数据标准化和跨学科合作的未来进展是实现ML在管理HFpEF方面的全部潜力的关键.
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