机器学习在心力衰竭诊断,预测和预后:回顾审查
Muhammad Saqib1, Prinka Perswani2, Abraar Muneem3
1Khyber Medical College, Peshawar.
Annals of medicine and surgery (2012)
|June 7, 2024
概括
机器学习 (ML) 有助于诊断和预测心力衰竭 (HF) 患者的结果. 使用生物标志物和基因表达的先进模型显示,有望识别高风险个体并改善HF管理.
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
- 计算生物学 计算生物学
背景情况:
- 心血管疾病,包括心肌梗塞 (MI) 和心力衰竭 (HF),每年导致超过1700万人死亡.
- 心力衰竭包括各种各样的亚型,如心力衰竭具有保留射出分数 (HFpEF),心力衰竭具有减少射出分数 (HFrEF) 和心力衰竭具有中等射出分数 (HFmrEF).
- 准确的诊断,预测和预后对于有效的HF管理和患者的结果至关重要.
研究的目的:
- 综合审查机器学习 (ML) 在心力衰竭 (HF) 的诊断,预测和预后中的应用.
- 探索ML在识别高风险HF患者和预测不同HF亚型的死亡率和住院率方面的作用.
- 综合当前对ML模型的研究,包括临床,生物标志物,基因表达和HF的表观遗传数据.
主要方法:
- 对将机器学习应用于心力衰竭诊断,预测和预后的研究进行系统性文献综述.
- 对不同HF群体 (ICU,HFpEF,HFrEF,HFmrEF) 使用的各种ML方法的分析.
- 检查使用临床数据,生物标志物,基因表达和表观遗传因素的预测模型.
主要成果:
- 机器学习在诊断HF亚型,包括HFpEF方面显示出显著的潜力.
- ML模型在预测死亡率和识别各种高风险患者中的高风险患者方面表现有前途.
- 结合临床评估,实验室评估和先进数据 (生物标记物,基因表达,表观遗传学) 的多维方法可以提高预测准确度.
结论:
- 综合临床,实验室和综合研究数据的多维策略对于推动HF理解和管理至关重要.
- 包含生物标志物,基因表达和表观遗传学的有希望的ML驱动的预测模型可以改善死亡率估计和高风险患者识别,特别是在HFpEF中.
- 本综述为专业人士提供了宝贵的资源,他们希望了解ML在整个频谱的HF诊断,预测和预后中的作用.
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