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-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 condition

SUBMITTER: Smith A 

PROVIDER: S-EPMC12271452 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature