Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study
Abstract Identifying clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help healthcare services to tailor care. In this population-based study, we developed a pipeline incorporating a DeBERTa language model to generate gender-specific clusters. Our model, EHR-DeB...
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| Format: | Article |
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Nature Portfolio
2025-07-01
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| Series: | npj Digital Medicine |
| Online Access: | https://doi.org/10.1038/s41746-025-01806-9 |
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| author | Alexander Smith Thomas Beaney Carinna Hockham Bowen Su Paul Elliott Laura Downey Spiros Denaxas Payam Barnaghi Abbas Dehghan Ioanna Tzoulaki |
| author_facet | Alexander Smith Thomas Beaney Carinna Hockham Bowen Su Paul Elliott Laura Downey Spiros Denaxas Payam Barnaghi Abbas Dehghan Ioanna Tzoulaki |
| author_sort | Alexander Smith |
| collection | DOAJ |
| description | Abstract Identifying clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help healthcare services to tailor care. In this population-based study, we developed a pipeline incorporating a DeBERTa language model to generate gender-specific clusters. Our model, EHR-DeBERTa, was pre-trained on longitudinal sequences of diagnoses, medications and test results from primary care electronic health records of 5.8 million patients in the UK. EHR-DeBERTa was used to generate patient embeddings for males and females separately, and clusters were identified by K-Means. Fifteen clusters were identified in females and seventeen in males, categorized into low disease burden, mental health, cardiometabolic, respiratory and mixed diseases. Cardiometabolic and mental health conditions showed the strongest separation across clusters, with older patients in cardiometabolic clusters. Our approach demonstrates how LLMs can provide interpretable insights into disease patterns. Future work incorporating clinical outcomes could enhance risk prediction and support precision-medicine for people with MLTC. |
| format | Article |
| id | doaj-art-cbd8646f6fcb459eb970de2e7c61d7de |
| institution | Kabale University |
| issn | 2398-6352 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | npj Digital Medicine |
| spelling | doaj-art-cbd8646f6fcb459eb970de2e7c61d7de2025-08-20T04:02:44ZengNature Portfolionpj Digital Medicine2398-63522025-07-018111310.1038/s41746-025-01806-9Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based studyAlexander Smith0Thomas Beaney1Carinna Hockham2Bowen Su3Paul Elliott4Laura Downey5Spiros Denaxas6Payam Barnaghi7Abbas Dehghan8Ioanna Tzoulaki9Department of Epidemiology and Biostatistics, Imperial College LondonThe George Institute for Global Health, Imperial College LondonThe George Institute for Global Health, Imperial College LondonDepartment of Surgery & Cancer, Imperial College LondonDepartment of Epidemiology and Biostatistics, Imperial College LondonThe George Institute for Global Health, Imperial College LondonInstitute of Health Informatics, University College LondonDepartment of Brian Sciences, Imperial College LondonDepartment of Epidemiology and Biostatistics, Imperial College LondonDepartment of Epidemiology and Biostatistics, Imperial College LondonAbstract Identifying clusters of people with similar patterns of Multiple Long-Term Conditions (MLTC) could help healthcare services to tailor care. In this population-based study, we developed a pipeline incorporating a DeBERTa language model to generate gender-specific clusters. Our model, EHR-DeBERTa, was pre-trained on longitudinal sequences of diagnoses, medications and test results from primary care electronic health records of 5.8 million patients in the UK. EHR-DeBERTa was used to generate patient embeddings for males and females separately, and clusters were identified by K-Means. Fifteen clusters were identified in females and seventeen in males, categorized into low disease burden, mental health, cardiometabolic, respiratory and mixed diseases. Cardiometabolic and mental health conditions showed the strongest separation across clusters, with older patients in cardiometabolic clusters. Our approach demonstrates how LLMs can provide interpretable insights into disease patterns. Future work incorporating clinical outcomes could enhance risk prediction and support precision-medicine for people with MLTC.https://doi.org/10.1038/s41746-025-01806-9 |
| spellingShingle | Alexander Smith Thomas Beaney Carinna Hockham Bowen Su Paul Elliott Laura Downey Spiros Denaxas Payam Barnaghi Abbas Dehghan Ioanna Tzoulaki Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study npj Digital Medicine |
| title | Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study |
| title_full | Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study |
| title_fullStr | Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study |
| title_full_unstemmed | Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study |
| title_short | Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based study |
| title_sort | identifying clusters of people with multiple long term conditions using large language models a population based study |
| url | https://doi.org/10.1038/s41746-025-01806-9 |
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