Clinic expert information provides important references for residents in need of hospital care. Usually, such information is hidden in the deep web and cannot be directly indexed by search engines. To extract clinic expert information from the deep web, the first challenge is to make a judgment on forms. This paper proposes a novel method based on a domain model, which is a tree structure constructed by the attributes of search interfaces. With this model, search interfaces can be classified to a domain and filled in with domain keywords. Another challenge is to extract information from the returned web pages indexed by search interfaces. To filter the noise information on a web page, a block importance model is proposed. The experiment results indicated that the domain model yielded a precision 10.83% higher than that of the rule-based method, whereas the block importance model yielded an F₁ measure 10.5% higher than that of the XPath method.
Hospital Information Systems ; Information Storage and Retrieval ; methods ; Internet ; User-Computer Interface

As regards the application of community residents electronics health record system, there is a question of how to draw the child growth monitoring chart quickly and efficiently, which is also the focus of this research for enhancing residents experience in using the system. The system is combined with the current emerging Ajax and GDI+ technology. The client uses the pre-designed Ajax Manager status to deal with residents' request and send XMLHTTP request to the server. Sever responds to the request and makes use of GDI+ programming for implementation of rendering graphics and feedback. The system finally realizes child growth monitoring chart releasing on the Web.
Automatic Data Processing ; methods ; Body Height ; Body Weight ; Child Development ; Electronic Health Records ; Female ; Humans ; Infant ; Infant, Newborn ; Male ; Monitoring, Physiologic

The scientificalness of page quality assessment was improved by adding the page vote fraction and the user satisfaction with page according to the time perception-based PageRank Algorithm. The individualized retrieval was realized by adding the users-interested model that was matched with page types and by moving up the users-interested page rank.