Lessons learned from building the iMED intelligent medical search engine.
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. iMed introduces and extends expert system technology into the search engine domain. It uses medical knowledge and an interactive questionnaire to help searchers form queries. This paper reports the lessons we learned from building the iMed system. We believe that many of these lessons can be applied to other medical search engines as well. We systematically discuss important issues in the new field consumer-centric intelligent medical search, including input interface, output interface, search system, medical knowledge base, help system, and testing.
Luo
Gang
G
IBM T.J. Watson Research Center, 19 Skyline Drive, Hawthorne, NY 10532, USA. luog@us.ibm.com
D003663:Decision Trees; D020407:Internet; D019359:Knowledge; D008490:Medical Informatics / Q000295:instrumentation*; D057386:Search Engine / Q000379:methods*; D011795:Surveys and Questionnaires