MedicoPort: A medical search engine for all

We present a new next generation domain search engine called MedicoPort. MedicoPort is a medical search engine designed for the users with no medical expertise. It is enhanced with the domain knowledge obtained from Unified Medical Language System (UMLS) to increase the effectiveness of the searches. The power of the system is based on the ability to understand the semantics of web pages and the user queries. MedicoPort transforms a keyword search into a conceptual search. Through our system we present a topical web crawling technique and indexing techniques empowered by the semantics information. MedicoPort aims to generate maximum output with semantic value using minimum input from the user. Since MedicoPort is designed to help people seeking information about health on the web, our target users are not medical specialists who can effectively use the special jargon of medicine and access medical databases. Medical experts have the advantage of shrinking the answer set by expressing several terms using medical terminology. MedicoPort provides the same advantage to its users through the automated use of the medical domain knowledge in the background. The results of our experiments indicate that, expanding the queries with domain knowledge, such as using the synonyms and partially or contextually relevant terms from UMLS, increase dramatically the relevance of an answer set produced by MedicoPort and the number of retrieved web pages that are relevant to the user request.

[1]  Hsinchun Chen,et al.  Meeting medical terminology needs-the ontology-enhanced Medical Concept Mapper , 2001, IEEE Transactions on Information Technology in Biomedicine.

[2]  Philip S. Yu,et al.  On the design of a learning crawler for topical resource discovery , 2001, TOIS.

[3]  Robert M Plovnick,et al.  Reformulation of Consumer Health Queries with Professional Terminology: A Pilot Study , 2004, Journal of medical Internet research.

[4]  Robert H. Baud,et al.  Health search engine with e-document analysis for reliable search results , 2006, Int. J. Medical Informatics.

[5]  Jochen R. Moehr,et al.  Towards improved information retrieval from medical sources , 1998, Int. J. Medical Informatics.

[6]  Qing Zeng-Treitler,et al.  Research Paper: Assisting Consumer Health Information Retrieval with Query Recommendations , 2006, J. Am. Medical Informatics Assoc..

[7]  Gerard Salton,et al.  A vector space model for automatic indexing , 1975, CACM.

[8]  T. Landauer,et al.  Indexing by Latent Semantic Analysis , 1990 .

[9]  Hsinchun Chen,et al.  CMedPort: An integrated approach to facilitating Chinese medical information seeking , 2006, Decis. Support Syst..

[10]  Christian Köhler,et al.  How do consumers search for and appraise health information on the world wide web? Qualitative study using focus groups, usability tests, and in-depth interviews , 2002, BMJ : British Medical Journal.

[11]  C Boyer,et al.  Health On the Net automated database of health and medical information. , 1997, International journal of medical informatics.

[12]  Lawrence M. Fagan,et al.  Empirical Formulation of a Generic Query Set for Clinical Information Retrieval Systems , 2001, MedInfo.

[13]  O Baujard,et al.  Trends in medical information retrieval on Internet. , 1998, Computers in biology and medicine.