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衡量互联网医生:根据社区知识对医学事实进行排名

Gauging the Internet Doctor: Ranking Medical Facts based on Community Knowledge
课程网址: http://videolectures.net/datamining2011_vydiswaran_gauging/  
主讲教师: V.G.Vinod Vydiswaran
开课单位: 伊利诺伊大学
开课时间: 2011-10-07
课程语种: 英语
中文简介:
随着越来越多的内容在线发布和消费,必须知道在Web上发现的信息块是否值得信赖。这对于在线医疗信息尤为重要,因为它会影响在线寻求医疗帮助的最脆弱群体用户。在本文中,我们研究了基于社区知识自动评估医疗索赔的可信度的可行性,并提出了基于对社区生成的集合的支持来为信息块分配可靠性分数的技术。具体而言,我们根据用户在健康论坛和邮件列表中分享的经验,对医疗索赔的可信度进行建模。建议的索赔分数可用于对相关可信度的相关索赔进行排名。我们进一步将可信度的概念扩展到站点(或等效地,来自站点的索赔数据库),并提出一种方案,基于聚合来自站点的索赔的信任分数来对站点进行排名。我们的实验表明,社区知识可以被利用来帮助用户区分可靠的医疗索赔和不可靠的医疗索赔。所提出的技术可以应用于可获得类似语料库的其他域。
课程简介: As more and more content is published and consumed online, it is imperative to know if an information nugget found on the Web is trustworthy or not. This is especially important for online medical information as it affects the most vulnerable group of users looking for medical help online. In this paper, we study the feasibility of automatically assessing the trustworthiness of a medical claim based on community knowledge, and propose techniques to assign a reliability score for an information nugget based on support over a community-generated collection. Specifically, we model the trustworthiness of a medical claim based on experiences shared by users in health forums and mailing lists. The proposed claim scores can be used to rank related claims on their relative trustworthiness. We further extend the notion of trustworthiness to a site (or equivalently, a database of claims from the site) and propose a scheme to rank sites based on aggregating the trust scores of claims from the site. Our experiments show that community knowledge can be exploited to help users distinguish reliable medical claims from unreliable ones. The proposed techniques can be applied to other domains where similar corpora are available.
关 键 词: 在线医疗信息; Web; 医疗索赔
课程来源: 视频讲座网
最后编审: 2020-07-14:yumf
阅读次数: 38