心血管风险评估中的机器学习:迈向精准医学方法
Yifan Wang1, Evmorfia Aivalioti2, Kimon Stamatelopoulos2,3
1Center for Molecular Cardiology, University of Zurich, Schlieren, Switzerland.
European journal of clinical investigation
|April 7, 2025
概括
人工智能和机器学习 (ML) 通过整合复杂的数据来增强心血管风险评估. 这些先进的模型有望为心血管疾病提供更精确,更个性化的预防策略.
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:
- 心血管疾病 (CVD) 是全球领先的健康负担.
- 目前的风险评分很难整合复杂的,多维的患者数据.
- 剩余的心血管风险需要先进的预测策略.
研究的目的:
- 审查机器学习 (ML) 在推进心血管风险评估中的作用.
- 探索ML在识别新型治疗点和改善预防方面的潜力.
- 讨论ML在心脏病学中的临床翻译的挑战和障碍.
主要方法:
- 对心血管风险中的人工智能和机器学习应用的审查.
- 整合各种数据类型:临床,心电图,成像和多omics.
- 利用深度神经网络进行高维数据分析和精确的表型.
主要成果:
- 机器学习模型在不同人群的心血管风险评估中提供了更高的精度.
- 潜在的ML发现新的治疗目标和完善预防策略.
- 证明了向个性化心血管护理的范式转变.
结论:
- 机器学习对精密心脏病学具有变革性的潜力.
- 解决数据质量,透明度和验证等挑战对于临床翻译至关重要.
- 倡导个性化的心血管预防策略,超越传统方法.
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