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Related Experiment Video

Updated: Sep 15, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Identifying clusters of people with Multiple Long-Term Conditions using Large Language Models: a population-based

Alexander Smith1, Thomas Beaney2, Carinna Hockham2

  • 1Department of Epidemiology and Biostatistics, Imperial College London, London, UK.

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Summary

Researchers used a language model to identify distinct groups of people with multiple long-term conditions (MLTC). This approach helps tailor healthcare by revealing patterns in patient data, aiding precision medicine.

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Area of Science:

  • Computational linguistics
  • Health informatics
  • Population health

Background:

  • Identifying patterns in Multiple Long-Term Conditions (MLTC) is crucial for personalized healthcare.
  • Existing methods may not fully capture the complexity of co-occurring chronic diseases.

Purpose of the Study:

  • To develop and apply a language model for identifying gender-specific clusters of patients with MLTC.
  • To leverage electronic health record (EHR) data for uncovering distinct disease patterns.

Main Methods:

  • A pipeline incorporating a DeBERTa language model (EHR-DeBERTa) was developed.
  • The model was pre-trained on longitudinal EHR data from 5.8 million UK patients.
  • Gender-specific patient embeddings were generated and clusters identified using K-Means analysis.

Main Results:

  • Fifteen clusters were identified in females and seventeen in males.
  • Clusters were categorized into low disease burden, mental health, cardiometabolic, respiratory, and mixed disease groups.
  • Cardiometabolic and mental health conditions showed significant separation across clusters, with older patients prevalent in cardiometabolic groups.

Conclusions:

  • Large language models (LLMs) can offer interpretable insights into complex disease patterns.
  • This approach supports precision medicine for individuals with MLTC.
  • Future research incorporating clinical outcomes can enhance risk prediction.