ArticleHealth and quality of life outcomes2024
Use of advanced topic modeling to generate domains for a preference-based index in osteoarthritis.
Article in Health and quality of life outcomes, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Utilizing BERTopic modeling for concept discovery in the domain of gerotranscendence and solitude.Journal of biomedical semantics · 2026Article
- Utilizing BERTopic Modeling for Concept Discovery in the Domain of Gerotranscendence and Solitude.Research square · 2025Article
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5 authors.
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Abstract
backgroundHealth-related quality of life (HRQL) is an important endpoint when evaluating the effectiveness of interventions in people living with hip and knee osteoarthritis (OA). The aim of this study was to generate domains for a new OA-specific preference-based index of HRQL in people living with hip or knee OA.
methodsThe proposed HRQL index was based on a formative measurement model. The study included people aged 50 years and older, who reported being diagnosed with hip or knee OA. Participants reported the most important areas of their lives affected by OA. BERTopic method was used for topic modeling as part of Natural Language Processing. Hierarchical topic modeling was applied to merge similar topics together.
resultsA total of 102 people participated from across Canada. The participants had a mean age of 64.3 ± 7.6 years, and they reported having either knee (48.0%) or hip (16.7%) OA, or both (35.3%). Six major topics that affect the quality of life of people with OA emerged from the BERTopic analysis. Pain, going up and down stairs, walking, standing at home or work, sleep, and playing with grandchildren were the major concerns reported by people living with OA.
conclusionThis study used natural language processing to generate domains for a new OA-specific HRQL index that is based on the views of people living with hip or knee OA. Six domains important to people living with OA formed the construct of HRQL. The next steps will be to create items based on the topics generated from this analysis and elicit people's preferences for the different items.
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