数据驱动的决策,用于精确诊断消化系统疾病
Song Jiang1,2, Ting Wang1,2, Kun-He Zhang3,4
1Department of Gastroenterology, The First Affiliated Hospital of Nanchang University, No. 17, Yongwai Zheng Street, Nanchang, 330006, China.
Biomedical engineering online
|September 1, 2023
概括
机器学习通过分析来自omics技术的大型生物医学数据来增强精确医学. 数据驱动的临床决策在诊断和管理消化系统疾病方面表现有前途.
科学领域:
- 生物医学数据分析
- 精准医学是一门精准的医学.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 奥米克技术产生了大量的生物医学数据,超过了传统的统计方法的能力.
- 机器学习 (ML) 为大数据处理提供解决方案,包括缩小维度和特征提取.
- 机器学习可以开发自动化,数据驱动的临床决策系统.
研究的目的:
- 审查最近在数据驱动的临床决策中取得的进展,以准确诊断消化系统疾病.
- 讨论数据驱动决策在这个领域的方法和局限性.
主要方法:
- 关于机器学习应用在消化系统疾病诊断中的最新文献的综述.
- 介绍数据驱动决策的方法.
- 讨论这些方法的潜力和挑战.
主要成果:
- 数据驱动的决策在各种消化系统疾病应用中显示出显著的前景.
- 应用包括早期诊断,分子类型,恶性瘤的分期和克罗恩病的诊断.
- 辅助诊断,差异诊断和实时功能评估也是应用领域.
结论:
- 机器学习驱动的临床决策对于推进消化系统疾病的精准医学至关重要.
- 尽管前景充满希望,但数据驱动决策的局限性需要进一步调查.
- 需要继续进行研究,才能在临床实践中充分发挥这些技术的潜力.
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