Clinic expert information extraction based on domain model and block importance model. (1st November 2015)

• Record Type: Journal Article
• Authors: Zhang, Yuanpeng; Wang, Li; Qian, Danmin; Geng, Xingyun; Yao, Dengfu; Dong, Jiancheng
• Is Part Of: Computers in biology and medicine. Volume 66(2015)
• Page Start: 337
• Page End: 342
• Publication Date: 2015-11-01
• Subjects: Information extraction -- Clinic expert information -- Domain model -- Block importance model -- SVM; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2015.07.009
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: To extract expert clinic information from the Deep Web, there are two challenges to face. The first one is to make a judgment on forms. A novel method based on a domain model, which is a tree structure constructed by the attributes of query interfaces is proposed. With this model, query interfaces can be classified to a domain and filled in with domain keywords. Another challenge is to extract information from response Web pages indexed by query interfaces. To filter the noisy information on a Web page, a block importance model is proposed, both content and spatial features are taken into account in this model. The experimental results indicate that the domain model yields a precision 4.89% higher than that of the rule-based method, whereas the block importance model yields an F1 measure 10.5% higher than that of the XPath method. Highlights: Clinic expert information provides references for residents who need hospital care. A domain model was defined to identify Web Query Interfaces. A virtual cluster was established for improving the performance of the domain model. A block importance model was proposed to filter noisy information in a Web page.