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GIOSUE' LO BOSCO

Finding the best web medical content for different learner categories

  • Autori: Alfano, M.; LO BOSCO, G.; Lenzitti, B.
  • Anno di pubblicazione: 2015
  • Tipologia: Abstract in atti di convegno pubblicato in volume
  • OA Link: http://hdl.handle.net/10447/138339

Abstract

In the age of Internet where any kind of information can be easily found online, it is becoming increasingly evident that more and more people use the World Wide Web to seek health and medical information for understanding and learning. Different users have diverse needs, even when searching for the same topic. This is certainly true in healthcare, where a patient, a physician or a health executive might look for information on the same topic but have different necessities and bring different levels of reading ability and prior knowledge together with a different vocabulary. Generic search engines (like Google, Bing or Yahoo) work on the whole web but make generic searches often overloading the user with the provided amount of information. Moreover, they are not able to provide specific information to different types of users. On the other band, specific search engines, such as PubMed or Quertle, work only on medical literature (mostly PubMed). They provide extracts from medical journals that are mainly useful for medical researchers and experts but do not consider all the information contained in the web that can often provide additional insights to the specific research domain being explored. Internet users looking for medical information would greatly benefit from a search engine that provides them with the 'right' information they are looking for without getting 'lost' with the amount and quality of information that Internet provides. To this end, we have developed a web search engine that 'drives' the search path of different learner categories by classifying the web pages on the basis of their level of health information and used language. In particular, we divide the web pages retrieved by a generic search engine in four categories: high level of medical content for experts, high level of medical content for consumers, low level of medical content and no medical content. We have implemented this system and carried out some experiments that show the effectiveness of our system when compared with the results carried out through human analysis