在消费者健康搜索中可解释的在线健康信息真实性
Rishabh Upadhyay1, Petr Knoth2, Gabriella Pasi1
1Information and Knowledge Representation, Retrieval, and Reasoning (IKR3) Lab, Department of Informatics, Systems, and Communication, University of Milano-Bicocca, Milan, Italy.
Frontiers in artificial intelligence
|July 7, 2023
概括
这项研究引入了评估在线健康信息的新方法,超越了简单的真假标签. 增强的系统提供了可解释的结果,提高了用户的信任和对健康信息准确性的理解.
科学领域:
- 信息科学 信息科学 信息科学
- 医疗信息学 医疗信息学
- 计算机科学 计算机科学
背景情况:
- 在线健康信息对公共卫生决策产生重大影响.
- 目前的系统经常使用二进制分类 (true/false) 来对健康信息进行分类,缺乏可解释性.
- 现有的方法呈现不透明的结果,阻碍了用户的信任和决策.
研究的目的:
- 开发一种新的系统来评估在线健康信息的真实性.
- 解决当前健康信息评估工具中二进制分类和不透明结果的局限性.
- 为了提高消费者对健康信息检索的解释性.
主要方法:
- 该研究将健康信息评估重新定义为一次性检索任务,而不是分类任务.
- 一个包含真实性作为相关性维度的信息检索模型得到了扩展.
- 通过整合来自医学期刊的科学证据知识库来实现可解释性.
主要成果:
- 拟议的解决方案通过用户研究进行了定量和质量评估.
- 结果表明,该系统在提供可解释的文件排序列表方面具有有效性.
- 该系统提高了用户对健康信息的时事相关性和真实性的理解.
结论:
- 这种新的方法提高了消费者在线健康信息的解释性.
- 可解释的检索系统有助于用户做出更明智的健康决策.
- 这项工作有助于更可靠和透明的在线健康信息获取.
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