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Syndromic Analysis of Sepsis Cohorts Using Large Language Models.

Theodore R Pak1,2, Sanjat Kanjilal1,3, Caroline S McKenna1

  • 1Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, Massachusetts.

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|October 24, 2025
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Summary
This summary is machine-generated.

Large language models (LLMs) accurately extract patient symptoms from clinical notes, aiding in sepsis diagnosis and outcome prediction. This technology helps identify associations between symptoms, infections, and mortality.

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Area of Science:

  • Clinical Informatics
  • Artificial Intelligence in Medicine
  • Sepsis Research

Background:

  • Extracting patient signs and symptoms from clinical notes is crucial for sepsis care but challenging for large-scale studies.
  • Current methods struggle to efficiently process unstructured clinical text for symptom data.

Purpose of the Study:

  • To evaluate the efficacy of large language models (LLMs) in extracting presenting signs and symptoms from patient admission notes.
  • To analyze the association of extracted symptoms with infectious diagnoses, multidrug-resistant infections, and mortality in sepsis patients.

Main Methods:

  • A retrospective cohort study involving over 100,000 adult patients across 5 hospitals.
  • Utilized a large language model (LLaMA 3 8B) to extract up to 10 symptoms from admission notes.
  • Validated LLM-extracted symptom labels against manual physician review and analyzed associations with outcomes using logistic regression.

Main Results:

  • The LLM demonstrated high accuracy (99.3%) in extracting symptoms, with validated labels for 98.7% of patients.
  • Extracted symptoms clustered into syndromes that correlated with specific infection sources and multidrug-resistant organisms (MRSA, MDRGN).
  • Cardiopulmonary symptoms were linked to increased mortality (AOR 1.30), while skin/soft tissue symptoms increased MRSA risk (AOR 1.73).

Conclusions:

  • LLMs can accurately extract patient signs and symptoms from admission notes, forming syndromes associated with infection types and patient outcomes.
  • This approach enables large-scale analysis of symptom data, potentially improving sepsis management and antibiotic strategies.
  • Further research is recommended to integrate this symptom data into predictive models for antibiotic choice and patient outcomes.