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衡量网络医生:基于社区知识的医学事实排名

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网上发布和消费,必须知道在网上找到的信息是否可信。这对于在线医疗信息尤其重要,因为它会影响到在线寻求医疗帮助的最弱势用户群体。本文研究了基于社区知识自动评估医疗索赔可信度的可行性,并提出了基于对社区生成集合的支持度来为信息块分配可靠性得分的技术。具体地说,我们根据用户在健康论坛和邮件列表中共享的经验来建模医疗索赔的可信度。提出的索赔分数可以用来排序相关索赔的相对可信度。我们进一步将可信度的概念扩展到一个站点(或者等价地,一个来自站点的声明数据库),并提出了一个基于聚合来自站点的声明的信任分数的站点排序方案。我们的实验表明,社区知识可以用来帮助用户区分可靠的医疗索赔和不可靠的索赔。所提出的技术可以应用于其他领域的相似语料库是可用的。
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: 医疗索赔; 信息块; 可信度
课程来源: 视频讲座网
数据采集: 2020-12-29:yxd
最后编审: 2021-01-08:yumf
阅读次数: 29