Searching for medical information on the Web has become highly popular, but it remains a challenging task because searchers are often uncertain about their exact medical situations and unfamiliar with medical terminology. To address this challenge, we have built an intelligent medical Web search engine called iMed, which uses medical knowledge and an interactive questionnaire to help searchers form queries. This paper focuses on iMed's iterative search advisor, which integrates medical and linguistic knowledge to help searchers improve search results iteratively. Such an iterative process is common for general Web search, and especially crucial for medical Web search, because searchers often miss desired search results due to their limited medical knowledge and the task's inherent difficulty. iMed's iterative search advisor helps the searcher in several ways. First, relevant symptoms and signs are automatically suggested based on the searcher's description of his situation. Second, instead of taking for granted the searcher's answers to the questions, iMed ranks and recommends alternative answers according to their likelihoods of being the correct answers. Third, related MeSH medical phrases are suggested to help the searcher refine his situation description. We demonstrate the effectiveness of iMed's iterative search advisor by evaluating it using real medical case records and USMLE medical exam questions.
Another similar effort is REF , where Luo and Tang developed iMed, an intelligent medical Web search engine.
On iterative intelligent medical search
SIGIR '08
Computer Science