整合大规模数据分析用于心血管疾病预测:一个范围审查
Salam Bani Hani1, Muayyad M Ahmad2
1Department of Nursing, Faculty of Nursing, Irbid National University, Irbid, Jordan.
Healthcare informatics research
|November 20, 2025
概括
大规模的数据分析和机器学习为心血管疾病 (CVD) 预测提供了机会,但偏见和数据质量等挑战需要注意有效的医疗保健. 进一步的研究和合作是至关重要的.
科学领域:
- 心血管疾病的研究研究.
- 医疗信息学 医疗信息学
- 医学中的数据科学.
背景情况:
- 心血管疾病 (CVD) 构成了全球严重的健康负担.
- 早期检测和预防对于改善患者的治疗结果至关重要.
- 大规模数据分析和机器学习 (ML) 在提高心血管疾病的预测能力方面表现有前途.
研究的目的:
- 综合当前关于整合大规模数据分析用于心血管疾病预测的文献.
- 确定使用ML进行心血管疾病风险评估的关键主题,应用和挑战.
- 为采用预测分析提供信息,以更好地预防心血管疾病和早期检测.
主要方法:
- 使用了一个范围审查方法.
- 在Medline (PubMed),EBSCO,谷歌学者和威利在线图书馆进行的搜索.
- 关键词包括"大数据"",大数据"",心血管疾病"",预测"",机器学习算法"",人工智能"和"死亡率" (2020-2024年).
主要成果:
- 在262个被检索的物品中,包括了16个物品.
- 确定的主题:数据分析技术/ML算法,ML/AI在心血管疾病预测中的应用,以及大数据在改善护理质量的作用.
- ML显示了预测心血管疾病结果的潜力.
结论:
- 机器学习为心血管疾病预测提供了巨大的潜力,但也有局限性.
- ML可能并不总是最佳的,特别是当因果推理至关重要时.
- 解决偏见,数据质量,伦理和实施挑战对于公平的医疗保健至关重要.
- 跨学科的合作和方法的完善是必要的,以实现未来的进步.
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