综合性回顾心脏病预测:从2019年起进行的一项比较研究
Monali Gulhane1, Sandeep Kumar1, Shilpa Choudhary2
1Symbiosis Institute of Technology, Nagpur Campus, Symbiosis International (Deemed University), Pune, Maharashtra, India.
Artificial intelligence in medicine
|January 31, 2026
概括
本综述探讨了心脏病预测,从传统方法转向机器学习和深度学习. 它强调了更好的心血管疾病诊断和患者结果的挑战和未来研究需求.
科学领域:
- 心脏病学 心脏病学
- 人工智能在医学中的应用
- 生物医学信息学 生物医学信息学
背景情况:
- 心血管疾病 (CVD) 是全球主要的死亡原因,需要改进早期诊断策略.
- 对心脏病的传统诊断方法在准确性和及时性方面存在局限性.
- 先进的计算技术的兴起为预测建模提供了新的途径.
研究的目的:
- 系统地审查心脏病预测当前的最新技术.
- 分析从传统诊断技术向机器学习 (ML) 和深度学习 (DL) 方法的过渡.
- 确定CVD预测中的挑战和未来研究方向.
主要方法:
- 进行了全面的文献审查.
- 批判性地评估了各种预测算法的有效性和局限性.
- 研究了风险因素的作用,它们的相互作用,以及结石等并发症的影响.
主要成果:
- 机器学习和深度学习方法显示出提高心脏病预测准确性的巨大潜力.
- 挑战包括识别特定的风险因素和理解复杂的非线性相互作用.
- 心血管疾病和结石之间的相互作用为预测模型开发提供了一个新的领域.
结论:
- 深度学习方法为改善心脏病诊断精度提供了有希望的进展.
- 优化患者管理和结果可以通过增强的预测能力来实现.
- 提出了未来研究的路线图,重点关注心脏病学中先进的AI应用.
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