research-article

Using Machine Learning for Automatic Identification of Evidence-Based Health Information on the Web

Authors:

Majed M. Al-Jefri,

Pietro Ghezzi, and

Gulden UchyigitAuthors Info & Claims

DH '17: Proceedings of the 2017 International Conference on Digital Health

July 2017

Pages 167 - 174

https://doi.org/10.1145/3079452.3079470

Published: 02 July 2017 Publication History

Abstract

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.

References

[1]

American Medical Association. 2017. JAMA. (2017). Retrieved January 2, 2017 from http://jama.jamanetwork.com/journal.aspx

[2]

David Blumenthal. 2002. Doctors in a wired world: can professionalism survive connectivity? Milbank Quarterly 80, 3 (2002), 525--546.

[3]

Deborah Charnock and Sasha Shepperd. 2017. DISCERN. (2017). Retrieved February 20, 2017 from http://www.discern.org.uk/

[4]

Deborah Charnock, Sasha Shepperd, Gill Needham, and Robert Gann. 1999. DISCERN: an instrument for judging the quality of written consumer health information on treatment choices. Journal of epidemiology and community health 53, 2 (1999), 105--111.

[5]

Elizabeth Cohen. 2010. Your top health searches, asked and answered. (2010). Retrieved January 2, 2017 from http://edition.cnn.com/2010/HEALTH/10/21/top. health.searches.answered/

[6]

Corinna Cortes and Vladimir Vapnik. 1995. Support-vector networks. Machine learning 20, 3 (1995), 273--297.

Digital Library

[7]

Kathleen C Ellwood, Paula R Trumbo, and Claudine J Kavanaugh. 2010. How the US Food and Drug Administration evaluates the scientific evidence for health claims. Nutrition reviews 68, 2 (2010), 114--121.

[8]

Susannah Fox. 2013. Pew Research Center. (2013). Retrieved February 18, 2016 from http://www.pewinternet.org/files/old-media/Files/Reports/PIP

[9]

Arnaud Gaudinat, Natalia Grabar, Célia Boyer, and others. 2007. Machine learning approach for automatic quality criteria detection of health web pages. In Medinfo 2007: Proceedings of the 12th World Congress on Health (Medical) Informatics; Building Sustainable Health Systems. IOS Press, 705.

[10]

Evidence-Based Medicine Working Group and others. 1992. Evidence-based medicine. A new approach to teaching the practice of medicine. Jama 268, 17 (1992), 2420.

[11]

Health on the Net Foundation. 2016. Health on the Net. (2016). Retrieved February 20, 2017 from https://www.healthonnet.org/

[12]

Jeremy H Howick. 2011. The philosophy of evidence-based medicine. John Wiley & Sons.

[13]

Lexical Computing CZ s.r.o. 2017. Sketch Engine. (2017). Retrieved February 20, 2017 from http://www.sketchengine.co.uk

[14]

Ali Maki, Roger Evans, and Pietro Ghezzi. 2015. Bad news: analysis of the Quality of information on influenza Prevention returned by google in english and italian. Frontiers in immunology 6 (2015).

[15]

Tom M Mitchell. 1997. Machine learning. 1997. Burr Ridge, IL: McGraw Hill 45, 37 (1997), 870--877.

Digital Library

[16]

NLTK project. 2015. Natural Language Toolkit -- NLTK 3.0 documentation. (2015). Retrieved February 20, 2017 from http://www.nltk.org/

[17]

Irina Rish. 2001. An empirical study of the naive Bayes classifier. In IJCAI 2001 workshop on empirical methods in artificial intelligence, Vol. 3. IBM New York, 41--46.

[18]

scikit-learn Project. 2016. scikit-learn: machine learning in Python - scikitlearn 0.18.1 documentation. (2016). Retrieved February 20, 2017 from http: //scikit-learn.org/stable

[19]

Edward H Shortliffe, RB Altman, PF Brennan, B Davie, WM Detmer, V Florance, A Friede, M Frisse, J Glaser, J Huffman, and others. 2000. Networking health: Prescriptions for the Internet. Computer Science and Telecommunications Board, The National Academies. Washington, DC: The National Academies Press (2000).

[20]

Parikshit Sondhi, VG Vinod Vydiswaran, and ChengXiang Zhai. 2012. Reliability prediction of webpages in the medical domain. In European Conference on Information Retrieval. Springer, 219--231.

Digital Library

[21]

Songbo Tan. 2005. Neighbor-weighted k-nearest neighbor for unbalanced text corpus. Expert Systems with Applications 28, 4 (2005), 667--671.

Digital Library

[22]

Yunli Wang and Zhenkai Liu. 2007. Automatic detecting indicators for quality of health information on the Web. International Journal of Medical Informatics 76, 8 (2007), 575--582.

[23]

Yunli Wang and Rene Richard. 2007. Rule-based automatic criteria detection for assessing quality of online health information. Journal on Information Technology in Healthcare 5, 5 (2007), 288--299.

[24]

Mubashar Yaqub and Pietro Ghezzi. 2015. Adding dimensions to the analysis of the quality of health information of websites returned by Google: cluster analysis identifies patterns of websites according to their classification and the type of intervention described. Frontiers in public health 3 (2015), 204.

[25]

Guo-Xun Yuan, Chia-Hua Ho, and Chih-Jen Lin. 2012. Recent advances of large-scale linear classification. Proc. IEEE 100, 9 (2012), 2584--2603.

[26]

Yan Zhang, Yalin Sun, and Bo Xie. 2015. Quality of health information for consumers on the web: a systematic review of indicators, criteria, tools, and evaluation results. Journal of the Association for Information Science and Technology 66, 10 (2015), 2071--2084.

Digital Library

Cited By

Liu JZhang Y(2024)Modeling Health Video Consumption Behaviors on Social Media: Activities, Challenges, and CharacteristicsProceedings of the ACM on Human-Computer Interaction10.1145/36536998:CSCW1(1-28)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653699
Baqraf YKeikhosrokiani PAl-Rawashdeh M(2023)Evaluating online health information quality using machine learning and deep learning: A systematic literature reviewDIGITAL HEALTH10.1177/205520762312122969Online publication date: 20-Nov-2023
https://doi.org/10.1177/20552076231212296
Upadhyay RPasi GViviani M(2023)Vec4Cred: a model for health misinformation detection in web pagesMultimedia Tools and Applications10.1007/s11042-022-13368-z82:4(5271-5290)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-022-13368-z
Show More Cited By

Index Terms

Using Machine Learning for Automatic Identification of Evidence-Based Health Information on the Web
1. Computing methodologies
  1. Artificial intelligence
    1. Natural language processing
  2. Machine learning
    1. Learning paradigms
      1. Supervised learning

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cover image ACM Other conferences

DH '17: Proceedings of the 2017 International Conference on Digital Health

July 2017

256 pages

ISBN:9781450352499

DOI:10.1145/3079452

General Chair:
Patty Kostkova
University College London
,
Program Chairs:
Floriana Grasso
University of Liverpool
,
Carlos Castillo
Eurecat
,
Yelena Mejova
QCRI
,
Arnold Bosman
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Copyright © 2017 ACM.

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Published: 02 July 2017

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DH '17

DH '17: International Conference on Digital Health

July 2 - 5, 2017

London, United Kingdom

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Cited By

Liu JZhang Y(2024)Modeling Health Video Consumption Behaviors on Social Media: Activities, Challenges, and CharacteristicsProceedings of the ACM on Human-Computer Interaction10.1145/36536998:CSCW1(1-28)Online publication date: 26-Apr-2024
https://dl.acm.org/doi/10.1145/3653699
Baqraf YKeikhosrokiani PAl-Rawashdeh M(2023)Evaluating online health information quality using machine learning and deep learning: A systematic literature reviewDIGITAL HEALTH10.1177/205520762312122969Online publication date: 20-Nov-2023
https://doi.org/10.1177/20552076231212296
Upadhyay RPasi GViviani M(2023)Vec4Cred: a model for health misinformation detection in web pagesMultimedia Tools and Applications10.1007/s11042-022-13368-z82:4(5271-5290)Online publication date: 1-Feb-2023
https://dl.acm.org/doi/10.1007/s11042-022-13368-z
Jebadurai JLebina WShwetha V(2023)Assessing Depression Health Information Using Machine LearningInternet of Things10.1007/978-3-031-28475-5_5(45-53)Online publication date: 30-Mar-2023
https://doi.org/10.1007/978-3-031-28475-5_5
Shaikh SKhan MNizami M(2021)Using Patient Descriptions of 20 Most Common Diseases in Text Classification for Evidence-based Medicine2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC)10.1109/MAJICC53071.2021.9526252(1-8)Online publication date: 15-Jul-2021
https://doi.org/10.1109/MAJICC53071.2021.9526252
Al-Jefri MEvans RLee JGhezzi P(2020)Automatic Identification of Information Quality Metrics in Health News StoriesFrontiers in Public Health10.3389/fpubh.2020.5153478Online publication date: 18-Dec-2020
https://doi.org/10.3389/fpubh.2020.515347
Song SZhang YYu B(2020)Interventions to Support Consumer Evaluation of Online Health Information Credibility: A Scoping ReviewInternational Journal of Medical Informatics10.1016/j.ijmedinf.2020.104321(104321)Online publication date: Nov-2020
https://doi.org/10.1016/j.ijmedinf.2020.104321

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