title: Automatic information timeliness assessment of diabetes web sites by evidence based medicine.
abstract: Studies on health domain have shown that health websites provide imperfect information and give recommendations which are not up to date with the recent literature even when their last modified dates are quite recent. In this paper, we propose a framework which assesses the timeliness of the content of health websites automatically by evidence based medicine. Our aim is to assess the accordance of website contents with the current literature and information timeliness disregarding the update time stated on the websites. The proposed method is based on automatic term recognition, relevance feedback and information retrieval techniques in order to generate time-aware structured queries. We tested the framework on diabetes health web sites which were archived between 2006 and 2013 by Archive-it using American Diabetes Association's (ADA) guidelines. The results showed that the proposed framework achieves 65% and 77% accuracy in detecting the timeliness of the web content according to years and pre-determined time intervals respectively. Information seekers and web site owners may benefit from the proposed framework in finding relevant and up-to-date diabetes web sites.
authors.affiliation: Department of Information Systems, Informatics Institute, Middle East Technical University, Ankara, Turkey. Electronic address: rahimebelen@gmail.com.
authors.forename: Rahime Belen
authors.initials: RB
authors.lastname: Sağlam
authors.affiliation: Department of Information Systems, Informatics Institute, Middle East Technical University, Ankara, Turkey.
authors.forename: Tuğba
authors.initials: T
authors.lastname: Taşkaya Temizel
mesh_terms: D002986:Clinical Trials as Topic / Q000145:classification / Q000706:statistics & numerical data*; D066289:Data Curation / Q000145:classification / Q000706:statistics & numerical data*; D057225:Data Mining / Q000379:methods*; D003920:Diabetes Mellitus; D019317:Evidence-Based Medicine; D006801:Humans; D009323:Natural Language Processing; D010506:Periodicals as Topic / Q000145:classification / Q000706:statistics & numerical data*; D061108:Social Media / Q000145:classification / Q000706:statistics & numerical data*; D013997:Time Factors
keywords: Content-based retrieval; Diabetes; Medical information systems; Timeliness analysis