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Using machine learning for automatic identification of evidence-based health information on the web

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conference contribution
posted on 2023-06-09, 06:35 authored by Majed M Al-Jefri, Roger Evans, Pietro Ghezzi, Gulden Uchyigit
Automatic assessment of the quality of online health information is a need especially with the massive growth of online content. In this paper, we present an approach to assessing the quality of health webpages based on their content rather than on purely technical features, by applying machine learning techniques to the automatic identification of evidence-based health information. Several machine learning approaches were applied to learn classifiers using different combinations of features. Three datasets were used in this study for three different diseases, namely shingles, flu and migraine. The results obtained using the classifiers were promising in terms of precision and recall especially with diseases with few different pathogenic mechanisms.

History

Publication status

  • Published

File Version

  • Accepted version

Journal

Conference Proceedings of the 2017 International Conference on Digital Health; London, UK; 2-5 July 2017

Publisher

Association for Computing Machinery

Page range

167-174

ISBN

9781450352499

Department affiliated with

  • Clinical and Experimental Medicine Publications

Full text available

  • Yes

Peer reviewed?

  • Yes

Legacy Posted Date

2017-06-08

First Open Access (FOA) Date

2017-07-07

First Compliant Deposit (FCD) Date

2017-06-08

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