生物医学大数据技术,应用,以及精准医学的挑战:一篇综述
Xue Yang1, Kexin Huang1, Dewei Yang2
1Department of Pancreatic Surgery and West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu 610041 China.
Global challenges (Hoboken, NJ)
|January 15, 2024
概括
生物医学大数据为精准医学提供了机会,但也带来了分析挑战. 本综述探讨了最近的进展,数据源和计算方法,以克服这些障碍,以获得更好的医疗保健.
科学领域:
- 生物医学大数据分析
- 精准医学是一门精准的医学.
- 计算生物学 计算生物学
背景情况:
- 生物医学大数据的爆炸性增长为精准医学带来了机遇和挑战.
- 自动收集大规模的分子和临床数据需要新的计算方法.
- 关键的挑战包括数据维度,异质性,缺失值,类不平衡和可扩展性.
研究的目的:
- 审查近期应用大数据在精准医学中的进展和突破.
- 总结关键方面,包括内容,数据源,技术,工具,挑战和差距.
- 讨论影响精准医学大数据的九个关键领域.
主要方法:
- 文献综述和生物医学大数据近期进展的综合.
- 大数据应用在九个关键领域的分类.
- 分析大数据管理和解释方面的挑战和现有差距.
主要成果:
- 在利用大数据用于精准医学方面取得了重大进展.
- 九个领域,包括数据仓储,EMRs,成像,手术中的AI,omics,健康监测,知识图,公共卫生和安全,是至关重要的.
- 新型计算方法对于应对数据分析和解释挑战至关重要.
结论:
- 生物医学大数据的高效管理和分析对于推进精准医学至关重要.
- 解决数据异质性和可扩展性等挑战是释放大数据潜力的关键.
- 对于翻译应用程序,需要不断开发计算工具和策略.
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