Abstract
Online community-based health services accumulate a huge amount of unstructured health question answering (QA) records at a continuously increasing pace. The ability to organize these health QA records has been found to be effective for data access. The existing approaches for organizing information are often not applicable to health domain due to its domain nature as characterized by complex relation among entities, large vocabulary gap, and heterogeneity of users. To tackle these challenges, we propose a top-down organization scheme, which can automatically assign the unstructured health-related records into a hierarchy with prior domain knowledge. Besides automatic hierarchy prototype generation, it also enables each data instance to be associated with multiple leaf nodes and profiles each node with terminologies. Based on this scheme, we design a hierarchy-based health information retrieval system. Experiments on a real-world dataset demonstrate the effectiveness of our scheme in organizing health QA into a topic hierarchy and retrieving health QA records from the topic hierarchy.
Highlights
The emergence of online health information needs has given rise to the establishment of online health services
With prior domain knowledge, we propose a topdown organization scheme where skeleton hierarchy is automatically determined, multiple relations are enabled, and nodes are profiled with terminologies
Our proposed schemes significantly outperform hierarchical LDA (hLDA)
Summary
The emergence of online health information needs has given rise to the establishment of online health services. The second category is the community-based health services (CHSs), such as HealthTap and HaoDF4 These services allow health seekers to freely post health-oriented questions, and encourage doctors to provide quality answers. Compared to the former sources, CHSs have some intrinsic properties. Health seekers and doctors with diverse backgrounds tend to Several practical systems and research efforts have been dedicated to organizing community-contributed data [1, 2] Most of these efforts, suffer from the following limitations. Answer partitions health data into only nine main categories which are too general to summarize the diverse health information Some popular topics such as “pregnancy” cannot be directly browsed here, because they do not fall under the predefined fixed category structure.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: EURASIP Journal on Bioinformatics and Systems Biology
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
