摘要
【目的】设计并实现一种无监督的算法,对在线医疗咨询服务中医生反馈内容的准确性进行自动评估。【方法】基于大量的在线咨询记录构造词汇之间的共现关系,将其作为对给定咨询问题的标准反馈进行预测的统计模型。通过比较实际反馈和标准反馈之间的相似性,可以获得医生反馈内容的准确性。【结果】通过对"好大夫在线"上的咨询记录进行评估,并与人工标注结果比对,本文算法在"严格匹配"和"软匹配"两种条件下可分别得到41.0%和82.4%的准确率。【局限】缺乏对文本中词汇顺序相关信息的考虑。【结论】本文算法可以帮助患者更有效地判断在线医疗信息的准确性,提升患者的就医决策效果。
[Objective] This paper designs and implements an unsupervised algorithm to evaluate the information accuracy of physicians＇ feedbacks from online consulting service. [Methods] First, we identified word co-occurrence relationships based on large amount of online service records. Then, we built a statistical model to predict standard feedbacks for the given questions. Finally, we decided the accuracy of physicians＇ answers by calculating content similarity between real feedbacks and the standard ones. [Results] We examined the proposed algorithm with records from Haodf.com as well as manually labeled results. The accuracy rates were 41.0% and 82.4% for rigorous and relax matching. [Limitations] We did not include the word sequence information in the algorithm. [Conclusions] The proposed algorithm could help patients know the accuracy of online medical information and improve their healthcare decisions makings.
出处
《数据分析与知识发现》
CSSCI
CSCD
2017年第11期29-36,共8页
Data Analysis and Knowledge Discovery
基金
国家自然科学基金重点国际(地区)合作项目"开放网络下医疗资源配置和优化的模型
算法及应用研究"(项目编号:71520107003)的研究成果之一
