Name
Title | ||
|---|---|---|
Effect of the Named Entity Recognition and Sliding Window on the HONcode Automated Detection of HONcode Criteria for Mass Health Online Content. |
Abstract | ||
|---|---|---|
The Health On the Netâs 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 discrepancies mainly for two criteria: âsubmerged contentâ under the Complementarity criterion and âextremely low recallâ 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. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.5220/0005644301510158 | HEALTHINF |
Field | DocType | Citations |
Complementarity (molecular biology),Data mining,Sliding window protocol,Web page,Trustworthiness,Computer science,Code of conduct,Attribution,Artificial intelligence,Named-entity recognition,Machine learning,The Internet | Conference | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Célia Boyer | 1 | 66 | 10.97 |
| Ljiljana Dolamic | 2 | 125 | 10.84 |
| Patrick Ruch | 3 | 0 | 0.68 |
| Gilles Falquet | 4 | 171 | 50.91 |