Challenging issues in iterative intelligent medical search

Abstract

Searching for medical information on the Web is highly popular these days. To facilitate ordinary people to perform medical search and preliminary disease self-diagnosis, we have built an intelligent medical Web search engine called iMed. iMed introduces and extends pattern recognition and expert system technology into the search engine domain. It uses medical knowledge and an interactive questionnaire to help searchers form queries. Due to searchers' limited medical knowledge and the task's inherent difficulty, searchers often cannot find desired search results in a single pass and have to search iteratively for multiple passes. For this purpose, iMed provides an iterative search advisor that guides searchers to refine their inputs. Based on our experience in building and using iMed, this paper summarizes the common difficulties faced by ordinary medical information searchers and the research issues that deserve attention from people working in the pattern recognition and medical search areas. © 2008 IEEE.