通过基于深度语言模型的多维信息质量评估进行在线健康搜索:算法开发和验证
Boya Zhang1, Nona Naderi2, Rahul Mishra1
1Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.
JMIR AI
|June 14, 2024
概括
这项研究开发了一个深度学习模型,根据质量对在线健康信息进行排名,而不仅仅是相关性. 新模式显著改善了获取有用和可信的健康内容,减少了对有害错误信息的暴露.
科学领域:
- 信息检索 信息检索
- 医疗信息学 医疗信息学
- 人工智能的人工智能
背景情况:
- 在线健康信息充斥着错误信息,对寻求医疗建议的个人构成风险.
- 目前的信息检索系统主要根据查询-文档相关性对结果进行排名,而忽视了信息质量.
研究的目的:
- 通过深度学习研究一个多维信息质量检索模型.
- 提高在线健康信息搜索结果的有效性和安全性.
主要方法:
- 模拟在线健康搜索场景,使用32个健康查询和10亿个网络文档.
- 评估文档质量 (有用性,支持性,可信性),使用预先训练的语言模型对6030个人类注释的对进行评估.
- 通过转移学习和领域适应技术来评估绩效.
主要成果:
- 实用性模型在转移学习中最好地区分了有用和有害的文档 (+5.6%).
- 组合模型 (实用性,支持性,可信性) 在有用主题上显示出最大的区别 (+16.9%).
- 域调整表现出强的性能,帮助-伤害兼容性超过 +4.4%跨维度.
结论:
- 整合特定信息质量维度的自动排名模型可以提高健康信息的检索.
- 拟议的方法可以提高公众在线健康搜索的质量和安全性.
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