Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681299
Title: Text mining patient experiences from online health communities
Author: Greenwood, Mark
ISNI:       0000 0004 5919 7979
Awarding Body: Cardiff University
Current Institution: Cardiff University
Date of Award: 2015
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Abstract:
Social media has had an impact on how patients experience healthcare. Through online channels, patients are sharing information and their experiences with potentially large audiences all over the world. While sharing in this way may offer immediate benefits to themselves and their readership (e.g. other patients) these unprompted, self-authored accounts of illness are also an important resource for healthcare researchers. They offer unprecedented insight into understanding patients’experience of illness. Work has been undertaken through qualitative analysis in order to explore this source of data and utilising the information expressed through these media. However, the manual nature of the analysis means that scope is limited to a small proportion of the hundreds of thousands of authors who are creating content. In our research, we aim to explore utilising text mining to support traditional qualitative analysis of this data. Text mining uses a number of processes in order to extract useful facts from text and analyse patterns within – the ultimate aim is to generate new knowledge by analysing textual data en mass. We developed QuTiP – a Text Mining framework which can enable large scale qualitative analyses of patient narratives shared over social media. In this thesis, we describe QuTiP and our application of the framework to analyse the accounts of patients living with chronic lung disease. As well as a qualitative analysis, we describe our approaches to automated information extraction, term recognition and text classification in order to automatically extract relevant information from blog post data. Within the QuTiP framework, these individual automated approaches can be brought together to support further analyses of large social media datasets.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.681299  DOI: Not available
Keywords: QA75 Electronic computers. Computer science
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