用机器学习来早期检测慢性阻塞性肺病:一个叙述性回顾
Xueting Shen1, Huanbing Liu2,3
1Department of General Medicine, The First Affiliated Hospital of Nanchang University, Nanchang, 330000, China.
Respiratory research
|September 9, 2024
概括
机器学习 (ML) 为慢性阻塞性肺病 (COPD) 的早期查提供了有希望的策略,COPD是全球死亡的主要原因. 本综述探讨了ML的应用,以改善早期检测,并为未来的COPD查研究提供信息.
科学领域:
- 呼吸系统医学 呼吸系统医学
- 人工智能的人工智能
- 医疗信息学 医疗信息学
背景情况:
- 慢性阻塞性肺病 (COPD) 是一个重大的全球健康挑战,在全球死亡率上排名第三.
- 慢性肺炎给个人和医疗保健系统带来了巨大的负担.
- 早期检测对于有效的管理和改善患者结果至关重要.
研究的目的:
- 审查用于早期COPD查的机器学习 (ML) 的最新进展.
- 分析ML技术在COPD检测中的实际应用和优化.
- 为未来的COPD查策略的研究和开发提供框架.
主要方法:
- 关于用于COPD查的ML的国内和国际研究的全面文献搜索.
- 对专注于ML算法的应用,优化和未来前景的研究进行分析.
- 综合发现,以确定关键趋势和挑战.
主要成果:
- 机器学习显示了早期和准确的COPD查的巨大潜力.
- 目前正在探索各种ML技术,并正在努力优化其性能.
- 优化的主要领域包括数据质量,特征选择和算法概括性.
结论:
- 基于ML的方法已经准备好彻底改变早期COPD查.
- 需要进一步的研究来完善ML模型并将其整合到临床实践中.
- 建立一个强大的科学基础将加速开发有效的ML驱动的COPD查工具.
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