Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease

Greenwood, Mark, Elwyn, Glyn ORCID: https://orcid.org/0000-0002-0917-6286, Francis, Nicholas Andrew ORCID: https://orcid.org/0000-0001-8939-7312, Preece, Alun David ORCID: https://orcid.org/0000-0003-0349-9057 and Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885 2013. Automatic extraction of personal experiences from patients' blogs: A case study in chronic obstructive pulmonary disease. Presented at: Third International Conference on Social Computing and its Applications, Karlsruhe, Germany, 30th September - 2nd October 2013.

Full text not available from this repository.

Abstract

People with long-term illness such as chronic obstructive pulmonary disease (COPD) often use social media to document and share information, opinions and their experiences with others. Analysing the self-reported experiences of patients shared online has the potential to help medical researchers gain insight into some of the key issues affecting patients. However, the scale of health conversation taking place online poses considerable challenges to traditional content analysis. In this paper, we present a system which automates extraction of patient statements which refer to a personal experience. We applied a crowdsourcing methodology to create a set of 1770 annotated sentences from blog posts written by COPD patients. Our machine learning approach trained on lexical features successfully extracted sentences about patient experience with 93% precision and 80% recall (F-measure: 86%). Automatic annotation of sentences about patient experience can facilitate subsequent content analysis by highlighting the most relevant sentences to this particular problem.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics Medicine
Subjects:	Q Science > QA Mathematics > QA76 Computer software R Medicine > R Medicine (General)
Related URLs:	http://socialcloud.aifb.uni-karlsruhe.de...
Last Modified:	25 Oct 2022 08:11
URI:	https://orca.cardiff.ac.uk/id/eprint/51733

Citation Data

Cited 4 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item