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Affiliation

Lero, School of Computing, Dublin City University, Glasnevin Campus, Dublin, Ireland, Anghelos Centro Studi sulla Comunicazione, Palermo and Italy

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1 Papers

Facilitating Access to Health Web Pages with Different Language Complexity Levels

Topics: Data Analytics and Health; Open Data and Health Information; Case Studies in eHealth

Authors: Marco Alfano ¹ , Biagio Lenzitti ² , Davide Taibi ³ and Markus Helfert ⁴

Affiliations: ¹ Lero, School of Computing, Dublin City University, Glasnevin Campus, Dublin, Ireland, Anghelos Centro Studi sulla Comunicazione, Palermo and Italy , ² Dipartimento di Matematica e Informatica, Università di Palermo, Palermo and Italy , ³ Istituto per le Tecnologie Didattiche, Consiglio Nazionale delle Ricerche, Palermo and Italy , ⁴ Lero, School of Computing, Dublin City University, Glasnevin Campus, Dublin and Ireland

Digital Object Identifier: 10.5220/0007740301130123

Proceedings of ICT4AWE 2019, ISBN: 978-989-758-368-1

Quick Abstract

The number of people looking for health information on the Internet is constantly growing. When searching for health information, different types of users, such as patients, clinicians or medical researchers, have different needs and should easily find the information they are looking for based on their specific requirements. However, generic search engines do not make any distinction among the users and, often, overload them with the provided amount of information. On the other hand, specific search engines mostly work on medical literature and specialized web sites are often not free and contain focused information built by hand. This paper presents a method to facilitate the search of health information on the web so that users can easily and quickly find information based on their specific requirements. In particular, it allows different types of users to find health web pages with required language complexity levels. To this end, we first use the structured data contained in the web to classify health web pages based on different audience types such as, patients, clinicians and medical researchers. Next, we evaluate the language complexity levels of the different web pages. Finally, we propose a mapping between the language complexity levels and the different audience types that allows us to provide different types of users, e.g., experts and non-experts with tailored web pages in terms of language complexity.