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Understanding patterns of loneliness in older long-term care users using natural language processing with free text
Sam Rickman1, Jose-Luis Fernandez1, Juliette Malley1
1Care Policy and Evaluation Centre, The London School of Economics and Political Science, London, United Kingdom.
Plos One
|April 2, 2025
Summary
New natural language processing (NLP) models accurately identify loneliness and social isolation in older adults
Area of Science:
- Gerontology
- Computer Science
- Public Health
Background:
- Loneliness and social isolation are significant concerns for older adults, impacting well-being and mortality.
- Existing data on these issues within publicly funded long-term care settings is limited.
- Natural Language Processing (NLP) offers novel methods for analyzing unstructured text data.
Purpose of the Study:
- To develop and validate an NLP-based method for identifying loneliness and social isolation in administrative case notes of older adults.
- To assess the prevalence of loneliness and social isolation in a long-term care population using this new method.
- To explore the utility of this NLP measure in understanding service use and care needs.
Main Methods:
- Analysis of 1.1 million free-text case notes from 3,046 older adults in London (2008-2020).
- Application of three NLP techniques: document-term matrices, pre-trained embeddings, and transformer-based models.
- Validation of the best-performing bidirectional transformer model using F1 scores, survey data, and literature comparisons.
Main Results:
- The bidirectional transformer model achieved a high F1 score of 0.92, demonstrating strong performance in identifying loneliness and isolation.
- Approximately 43% of individuals had case notes indicating loneliness or isolation at initial assessment, aligning with survey estimates.
- The NLP-derived measure showed construct validity, correlating with factors like living alone and impaired memory, and predicting social inclusion service use.
Conclusions:
- NLP provides a robust and scalable method for detecting loneliness and social isolation in administrative health records.
- This approach overcomes limitations of traditional surveys by linking textual data to other administrative information.
- The open-source model facilitates further research and development of targeted interventions for vulnerable older adults.
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