Journal article

Text Mining for Personalized Knowledge Extraction From Online Support Groups

Tharindu Rukshan Bandaragoda, Daswin De Silva, Damminda Alahakoon, Weranja Ranasinghe, Damien Bolton

JOURNAL OF THE ASSOCIATION FOR INFORMATION SCIENCE AND TECHNOLOGY | WILEY | Published : 2018

Abstract

The traditional approach to health care is being revolutionized by the rapid adoption of patient-centered healthcare models. The successful transformation of patients from passive recipients to active participants is largely attributed to increased access to healthcare information. Online support groups present a platform to seek and exchange information in an inclusive environment. As the volume of text on online support groups continues to grow exponentially, it is imperative to improve the quality of retrieved information in terms of relevance, reliability, and usefulness. We present a text-mining approach that generates a knowledge extraction layer to address this void in personalized in..

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University of Melbourne Researchers

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Funding Acknowledgements

The authors would like to thank the anonymous reviewers and edition for their valuable comments and suggestions to improve the quality of this paper. This work was supported by an Australian Government Research Training Program Scholarship. Authors would also like to acknowledge the financial and in-kind support from the Data to Decisions Cooperative Research Centre (D2D CRC) as part of their analytics and decision support program.