Concept Embedding for Relevance Detection of Search Queries Regarding CHOP

摘要：

　　Automatic encoding of diagnosis and procedures can increase the interoperability and efficacy of the clinical cooperation.The concept,rule-based and machine learning classification methods for automatic code generation can easily reach their limit due to the handcrafted rules and a limited coverage of the vocabulary in a concept library.As the first step to apply deep learning methods in automatic encoding in the clinical domain,a suitable semantic representation should be generated.In this work,we will focus on the embedding mechanism and dimensional reduction method for text representation,which mitigate the sparseness of the data input in the clinical domain.Different methods such as word embedding and random projection will be evaluated based on logs of query-document matching.

关键词： Automatic Encoding Classification Machine Learning

作者: Yihan Deng Lukas Faulstich Kerstin Denecke

作者单位: Bern University of Applied Science,Bern,Switzerland ID Information and Documentation GmbH,Berlin,Germany

会议类型: 国际会议

会议名称: 第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)

会议地点: 苏州

会议语种:英文

页码: 1260-1260

在线出版日期: 2017-08-21（万方平台首次上网日期，不代表论文的发表时间）