机器学习方法在儿童喘恶化管理中的应用:系统性审查
Chunni Zhou1, Liu Shuai1, Hao Hu2
1School of Public Health, Southeast University, 87, Dingjiaqiao Road, Gulou District, Nanjing, 210009, China.
BMC medical informatics and decision making
|April 18, 2025
概括
机器学习 (ML) 技术在治疗儿童喘恶化方面显示出显著的前景. 这些先进的数据分析方法在诊断,个性化治疗和喘儿童的长期护理方面具有优势.
科学领域:
- 医疗信息学 医疗信息学
- 计算生物学 计算生物学
- 儿科肺病学 儿科肺病学
背景情况:
- 儿童喘恶化是一个重大的全球健康挑战,影响儿童的福祉和生活质量.
- 机器学习 (ML) 是一种复杂的数据分析方法,越来越多地被认为是它在医疗保健中的潜力.
- 本综述系统地评估了儿童喘恶化管理中的ML应用.
研究的目的:
- 评估ML技术在儿科喘恶化中的应用.
- 探索ML在这个临床领域的有效性和潜在价值.
- 为儿童呼吸系统健康提供先进数据分析的见解.
主要方法:
- 在PubMed,EBSCO,Elsevier和Web of Science数据库 (2000年1月至2025年1月) 中进行了系统的文献搜索.
- 符合条件的研究涉及应用到儿科喘恶化中的ML方法,并以英语出版.
- 使用有效公共卫生实践项目 (EPHPP) 工具评估了研究质量.
主要成果:
- 包括23项研究,使用各种ML模型,如决策树,神经网络和支持向量机器.
- 机器学习应用专注于风险因素分析,诊断,预测,医疗保健资源优化和综合管理.
- ML技术在儿童喘恶化管理和个性化医疗保健提供方面表现出显著的优势.
结论:
- 机器学习技术对儿童喘恶化有很大的前景.
- 进一步的研究和临床验证对于稳健的实施至关重要.
- 预计ML将显著支持诊断,个性化治疗和长期管理策略.
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