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将基因组学与心脏病学临床实践相结合:人工智能优化多基因风险得分:系统审查
Kaveh Hosseini1, Nazanin Anaraki1, Parham Dastjerdi1
1Tehran Heart Center, Cardiovascular Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran.
JACC. Advances
|June 27, 2025
概括
人工智能 (AI) 优化多基因风险评分 (PRS) 以更好地预测心血管疾病. 人工智能优化的PRS提高了准确性,改善了患者风险分层和个性化预防策略.
科学领域:
- 心血管疾病的研究研究.
- 遗传学和生物信息学 遗传学和生物信息学
- 医学中的人工智能
背景情况:
- 传统的心血管疾病风险模型在识别高风险个体方面存在局限性.
- 多基因风险评分 (PRS) 汇总遗传变异以量化遗传易感性,但面临实际挑战.
- 需要更精确的工具来分层心血管疾病风险.
研究的目的:
- 系统地审查人工智能 (AI) 和机器学习算法如何优化PRS (AI优化PRS) 以改善心血管疾病预测.
- 与非优化的PRS模型相比,评估AI优化PRS的性能.
- 评估AI优化的PRS在指导个性化预防策略方面的潜力.
主要方法:
- 对13项调查人工智能和机器学习以优化PRS的研究进行系统审查.
- 分析AI算法如何改善PRS模型中的功能选择,数据处理和变量集成.
- 人工智能优化的PRS与传统的PRS之间的预测准确性比较.
主要成果:
- 人工智能优化的PRS模型显示,对心血管疾病的预测准确度有所提高.
- 这些模型有效地处理高维基遗传数据,并整合各种变量 (临床因素,生物标志物,成像).
- 人工智能优化的PRS优于非优化的PRS,提供更全面的风险概况和更好的患者分层.
结论:
- 人工智能优化的PRS代表了心血管疾病风险预测的重大进步.
- 这些增强的分数可以改善高风险个体的识别,并为个性化预防策略提供信息.
- 未来的研究应侧重于性别差异,人口多样性,临床整合和成本效益.
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