机器学习在心血管风险预测和精确预防方法中的机器学习
Nitesh Gautam1, Joshua Mueller2, Omar Alqaisi3
1Division of Cardiology, Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 W. Markham St, Little Rock, AR, 72223, USA.
Current atherosclerosis reports
|November 26, 2023
概括
机器学习 (ML) 提高了使用大数据对冠状动脉疾病 (CAD) 的心血管风险预测. 解决ML挑战是将这项技术整合到临床实践中以获得更好的患者结果的关键.
科学领域:
- 心血管疾病研究研究
- 人工智能在医学中的应用
- 生物医学数据科学 生物医学数据科学
背景情况:
- 冠状动脉疾病 (CAD) 影响着数百万人,造成了重大的社会经济负担.
- 目前对CAD的患者级风险模型缺乏最佳的准确性和实用性.
- 人们对利用人工智能 (AI) 和大数据来预测CAD风险的兴趣越来越大.
研究的目的:
- 在心血管风险预测中提供机器学习 (ML) 应用的概述.
- 专注于心血管疾病的精确预防方法.
- 突出ML的局限性和在治疗冠状动脉疾病 (CAD) 的未来潜力.
主要方法:
- 对ML在心血管风险预测中的当代应用进行审查.
- 利用大数据方法与人工智能技术相结合.
- 采用ML算法来分析多维,个性化的患者数据,包括代谢和基因组资料.
主要成果:
- 目前正在开发ML模型,以提高CAD风险预测的准确性和实用性.
- 通过利用特定的患者数据,ML可以实现个性化的患者护理方法.
- ML显示了整合CAD多方面的潜力,以改善患者的治疗结果.
结论:
- ML提供了一个转向患者特异性心血管护理的范式转变.
- 解决 ML 整合到医疗保健中的固有挑战对于广泛的临床采用至关重要.
- 在CAD管理中,ML有可能显著改善患者水平的结果和人口健康.
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