...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Journal of Applied Research and Technology >Patient opinion mining to analyze drugs satisfaction using supervised learning
【24h】

Patient opinion mining to analyze drugs satisfaction using supervised learning

机译：患者意见挖掘，通过监督学习来分析药物满意度

获取原文
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Abstract Opinion mining is a very challenging problem, since user generated content is described in various complex ways using natural language. In opinion mining, most of the researchers have worked on general domains such as electronic products, movies, and restaurants reviews but not much on health and medical domains. Patients using drugs are often looking for stories from patients like them on the internet which they cannot always find among their mends and family. Few studies investigating the impact of social media on patients have shown that for some health problems, online community support results in a positive effect. The opinion mining method employed in this work focuses on predicting the drug satisfaction level among the other patients who already experienced the effect of a drug. This work aims to apply neural network based methods for opinion mining from social web in health care domain. We have extracted the reviews of two different drugs. Experimental analysis is done to analyze the performance of classification methods on reviews of two different drugs. The results demonstrate that neural network based opinion mining approach outperforms the support vector machine method in terms of precision, recall and f-score. It is also shown that the performance of radial basis function neural network method is superior than probabilistic neural network method in terms of the performance measures used.
机译：摘要意见挖掘是一个非常具有挑战性的问题，因为使用自然语言以各种复杂的方式描述了用户生成的内容。在观点挖掘中，大多数研究人员都在诸如电子产品，电影和饭店评论之类的一般领域工作，但在健康和医疗领域则工作不多。吸毒的患者经常在互联网上寻找像他们这样的患者的故事，而他们在他们的家人和家人中总是找不到。很少有研究调查社交媒体对患者的影响的研究表明，对于某些健康问题，在线社区支持会产生积极影响。在这项工作中采用的意见挖掘方法着重于预测已经经历过药物作用的其他患者之间的药物满意度。这项工作旨在将基于神经网络的方法应用于医疗保健领域的社交网络中的观点挖掘。我们提取了两种不同药物的评价。进行了实验分析，以分析分类方法对两种不同药物的评价的性能。结果表明，基于神经网络的观点挖掘方法在准确性，召回率和f评分方面优于支持向量机方法。还表明，就使用的性能指标而言，径向基函数神经网络方法的性能优于概率神经网络方法。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号