摘要
随着移动互联网技术和医疗社区平台的普及,越来越多的市民在去医院就诊前会上医疗社区平台进行症状查询或者寻医咨询。医疗社区平台上的商业导向、广告植入乃至无良偏方很容易诱导患者采用不恰当的治疗手段。针对这些信息给综合检索平台的通用医疗信息检索带来了巨大噪声的问题,提出一种基于医疗社区平台信息提供方的可信评价机制。该方法通过分析医疗咨询信息提供者的专业等级、关注领域、信息认可度等社区平台公开数据对一个医疗社区问答集中的多个回答进行排序筛选,解决了医疗社区问答系统中"一问多答"现象给检索系统带来的干扰;同时将新的医疗咨询检索内容进行科室分类,并与信息提供方的关注领域进行匹配,从而有效提高了检索系统对医疗社区平台问答数据的检索命中率。
With the rapid development of moile Internet tehnology and online health community(OHC),more and more patients and caregivers would search the health information and seek medical advice before going to hsopital.However,there will be plenty of answers for patients and they may be influenced by unrelated advertisements,inaccurate suggestions and unreliable regiments.In order to reduce the noise of unreliable data for sorting algorithm,this paper proposed a new algorithm to optimize the ranking of searching results with some credible information on OHC platforms.The method utilizes the information of every OHC answer provider,including professional knowledge level,focused fields,answer-accepted rate,and so on,to estimate a credible score.For each new question searching,a combined sorting function with the content similarity and credible score for provider is provided to obtain the results ranking.To improve the accuracy in a further step,the category of searching question is given to match the interested area of answer provider.The experiment compares several optimizing factors and their corresponding results,and the results show that this new algorithm can effectively select more accurate answers on OHC platforms.
作者
曹艳蓉
章韵
李涛
李华康
CAO Yan-rong;ZHANG Yun;LI Tao;LI Hua-kang(Institute of Computer Software,Nanjing University of Posts and Telecommunications,Nanjing 210003,China;Jiangsu Province Key Lab of Big Data Security and Intelligent Processing,Nanjing 210003,China)
出处
《计算机科学》
CSCD
北大核心
2018年第10期150-154,共5页
Computer Science
基金
国家自然科学基金(61502247
11501302
61502243
91646116)
中国博士后科学基金(2016M600434)
江苏省科技支撑计划(社会发展)项目(BE2016776)
江苏省"六大人才高峰"项目(XYDXXJS-CXTD-006)
江苏省博士后科研基金(1601128B)资助
关键词
可信评价
医疗社区
一问多答
科室分类
Credible evaluation
Online health community
One-question multi-answer
Office classification
