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Healthc Inform Res > Volume 15(3); 2009 > Article
Journal of Korean Society of Medical Informatics 2009;15(3):247-254.
DOI: https://doi.org/10.4258/jksmi.2009.15.3.247    Published online September 30, 2009.
The Feasibility of Using Classification and Identification Techniques to Auto-Assess the Quality of Health Information on the Web
Polun Chang1, Fan Pin Huang1,2, Min Ling Lai1,3
1Institute of Biomedical Informatics, National Yang-Ming University, Taiwan/ROC.
2Armed Forces Taichung General Hospital, Taiwan/ROC.
3Department of Information Technology, Taipei City Government, Taiwan/ROC.
Correspondence:  Polun Chang,
Received: 29 September 2009

Objective: An automatic detection tool was created for examining health-related webpage quality we went further by examining its feasibility and performance.

Methods: We developed an automatic detection system to auto-assess the authorship quality indicator of an health-related information webpage for governmental websites in Taiwan. The system was integrated with the Chinese word segmentation system developed by the Academia Sinica in Taiwan and the SVMlight, which serve as an SVM (Support Vector Machine) Classifiers and a method of information extraction and identification. The system was coded in Visual Basic 6.0, using SQL 2000.

Results: We developed the first Chinese automatic webpage classification and information identifier to evaluate the quality of web information. The sensitivity and specificity of the classifier on the training set of webpages were both as high as 100% and only one health webpage in the test set was misclassified, due to the fact that it contained both health and non-health information content. The sensitivity of our authorship identifier is 75.3%, with a specificity of 87.9%.

Conclusion: The technical feasibility of auto-assessment for the quality of health information on the web is acceptable. Although it is not sufficient to assure the total quality of web contents, it is good enough to be used to support the entire quality assurance program.

Key Words: Health Information Quality, Automatic Assessment, Categorization, Information Extraction


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