Closing in on open-ended patient questionnaires with text mining

Spasic, Irena ORCID: https://orcid.org/0000-0002-8132-3885, Owen, David ORCID: https://orcid.org/0000-0002-4028-0591, Smith, Andrew ORCID: https://orcid.org/0000-0001-8805-8028 and Button, Kate ORCID: https://orcid.org/0000-0003-1073-9901 2018. Closing in on open-ended patient questionnaires with text mining. Presented at: UK Healthcare Text Analytics Conference (HealTAC), Manchester, UK, 18-19 April 2018.

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Abstract

Knee injury and Osteoarthritis Outcome Score (KOOS) is an instrument used to quantify patients' perceptions about their knee condition and associated problems. It is administered as a 42-item closed-ended questionnaire in which patients are asked to self-assess five outcomes: pain, other symptoms, activities of daily living, sport and recreation activities, and quality of life. We developed KLOG as a 10-item open-ended version of the KOOS questionnaire in an attempt to obtain deeper insight into patients’ opinions including their unmet needs. However, the open–ended nature of the questionnaire incurs analytical overhead associated with the interpretation of responses. The goal of this study was to automate such analysis. To that end, we implemented KLOSURE as a system for mining free–text responses to the KLOG questionnaire. The precision of the system varied between 64.8% and 95.3%, whereas the recall varied from 61.3% to 87.8% across the 10 questions.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Completion
Status:	Unpublished
Schools:	Computer Science & Informatics Healthcare Sciences Psychology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software
Uncontrolled Keywords:	text mining, text classification, natural language processing
Funders:	Welcome Trust
Last Modified:	22 Nov 2022 09:28
URI:	https://orca.cardiff.ac.uk/id/eprint/109733

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