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Authors: Celia Boyer 1 ; Ljiljana Dolamic 1 ; Patrick Ruch 2 and Gilles Falquet 3

Affiliations: 1 Health On the Net Foundation, Switzerland ; 2 HES-SO Geneva and SIB Swiss Institute of Bioinformatics, Switzerland ; 3 University of Geneva, Switzerland

ISBN: 978-989-758-170-0

Keyword(s): HONCODE, Automated Detection, Manual Detection, Machine Learning, Named Entity Recognition.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Business Analytics ; Cloud Computing ; Data Engineering ; Data Mining ; Databases and Information Systems Integration ; Datamining ; e-Health ; Enterprise Information Systems ; Health Information Systems ; Platforms and Applications ; Sensor Networks ; Signal Processing ; Soft Computing

Abstract: The Health On the Net’s Foundation (HON) Code of Conduct, HONcode, is the oldest and the most used ethical and trustworthy code for medical and health related information available on the Internet. Until recently, websites voluntarily applying for the HONcode seal were evaluated manually by an expert medical team according to 8 principles, referred to as criteria, and associated published guidelines. In the scope of the European project Kconnect, HON is developing an automated system to identify the 8 HONcode criteria within health webpages. When the research on the development of such a system evolved from simple algorithmic testing to a real full-content setting, it revealed a number of issues. The preceding study consisted in taking a set of 27 health-related websites and having them assessed for their compliance to each of the 8 HONcode criterion, first manually by senior HONcode experts, and then through supervised machine learning by the automated system. The results showed disc repancies mainly for two criteria: “submerged content” under the Complementarity criterion and “extremely low recall” under the Date Attribution criterion. In this article, the authors investigate different approaches to solve the problems related to each of these criteria, namely a customized Named Entity Recognition Model instead of a machine learning component for Date Attribution, and a sliding window instead of the whole document as a unit of detection for Complementarity. The results obtained show that the newly adapted automated system greatly improves accuracy: 74% vs. 41% for the Date Attribution criterion and 74% vs. 22% for the Complementarity criterion. (More)

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Paper citation in several formats:
Boyer, C.; Dolamic, L.; Ruch , P. and Falquet, G. (2016). Effect of the Named Entity Recognition and Sliding Window on the HONcode Automated Detection of HONcode Criteria for Mass Health Online Content.In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, (BIOSTEC 2016) ISBN 978-989-758-170-0, pages 151-158. DOI: 10.5220/0005644301510158

@conference{healthinf16,
author={Celia Boyer. and Ljiljana Dolamic. and Patrick Ruch . and Gilles Falquet.},
title={Effect of the Named Entity Recognition and Sliding Window on the HONcode Automated Detection of HONcode Criteria for Mass Health Online Content},
booktitle={Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, (BIOSTEC 2016)},
year={2016},
pages={151-158},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005644301510158},
isbn={978-989-758-170-0},
}

TY - CONF

JO - Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies - Volume 5 HEALTHINF: HEALTHINF, (BIOSTEC 2016)
TI - Effect of the Named Entity Recognition and Sliding Window on the HONcode Automated Detection of HONcode Criteria for Mass Health Online Content
SN - 978-989-758-170-0
AU - Boyer, C.
AU - Dolamic, L.
AU - Ruch , P.
AU - Falquet, G.
PY - 2016
SP - 151
EP - 158
DO - 10.5220/0005644301510158

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