基于机器学习的人工智能工具用于热带热病诊断:对诊断测试准确性的系统审查和元分析协议
Shravya Chitrapady1, Rajalakshmi Rajendran1, Haritha K1
1Department of Pharmacy Practice, Manipal College of Pharmaceutical Sciences, Manipal Academy of Higher Education, Manipal, Karnataka, India.
本系统审查协议概述了用于诊断热带热病的人工智能 (AI) 和机器学习 (ML) 工具的评估. 它旨在汇集各种热带疾病的基于人工智能的诊断准确性研究的性能指标.
科学领域:
- 医疗信息学
- 传染性疾病
- 诊断的准确性
背景情况:
- 人工智能 (AI) 正在推进热带热病诊断,旨在减少疾病负担.
- 机器学习 (ML) 模型在诊断单个或多个热带发烧方面表现有前途.
- 本协议详细介绍了基于人工智能的诊断测试准确性研究的系统审查.
研究的目的:
- 系统审查基于人工智能的机器学习 (ML) 诊断测试准确性研究.
- 确定用于评估热带热病的AI诊断工具的关键因素.
- 汇集并评估可用的基于机器学习的AI诊断设备的性能.
主要方法:
- 在Medline/PubMed,Embase,Cochrane和Scopus系统搜索热带热病诊断ML工具的研究.
- 包含具有横截面设计的原始研究,重点是AI/ML诊断准确性.
- 使用Meta-Disc软件进行定量数据的叙述综合和元分析;使用QUADAS-2工具进行质量评估.
主要成果:
- 该审查将综合基于ML的AI工具用于诊断热带发烧的数据.
- 不同的人工智能诊断工具的性能指标将被合并.
- 该研究将概述当前基于ML的AI设备的诊断准确性.
结论:
- 这一系统性审查将为热带热病诊断AI/ML工具的性能提供关键见解.
- 这些发现将为人工智能驱动的诊断解决方案的开发和实施提供信息.
- 这项审查将有助于了解人工智能在管理热带传染病方面的潜力.
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