Personalized health document summarization exploiting UMLS and topic-based clustering for mobile healthcare
According to the growing interest in mobile healthcare, multi-document summarization techniques are increasingly required to cope with health information overload and effectively deliver personalized online healthcare information. However, because of the peculiarities of medical terminology and the diversity of subtopics in health documents, multi-document summarization must consider technical aspects that are different from those of the general domain.In this work, we had proposed a personalized health document summarization system that provides a reliable personal health-related summary to general healthcare consumers via mobile devices.
Our system generates a personalized summary from multiple online health documents by exploiting biomedical concepts, semantic types, and semantic relations extracted from the Unified Medical Language System(UMLS) and analysing individual health records derived from mobile personal health record(PHR) applications. Furthermore, to increase the diversity and coverage of summarized results and to display them in a user-friendly manner on mobile devices, we create a summary that is categorized into subtopics by grouping semantically related sentences through topic-based clustering. The experimental evaluations demonstrate the effectiveness of our proposed system.
