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September 10, 2025Infection Control and Hospital Epidemiology2 citations

Extracting antibiotic susceptibility from free-text microbiology reports using natural language processing

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ACAndrew ChouRHRonald G. HauserLBLori A. Bastian

Key Points

  • Natural language processing efficiently identifies antibiotic susceptibility from free-text microbiology reports, improving detection efforts.
  • Using large language models, the study achieved significant advancements in extracting relevant information from clinical documents.
  • The method enables faster outbreak detection and enhances public health reporting through improved data utilization.
  • The application of machine learning in this healthcare context suggests potentially transformative impacts on managing infectious diseases.

Abstract

Abstract There is a clinical need to appropriately apply large language model (LLM)-based systems for use in infectious diseases. We sought to use LLM and machine learning for extracting antibiotic susceptibility from clinical microbiology free-text reports, allowing use for outbreak detection, increasing information gathering efficiency, and public health reporting.

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Cite This Study

Chou et al. (2025) studied this question.

synapsesocial.com/papers/68c1a26954b1d3bfb60dd797https://doi.org/10.1017/ice.2025.10210
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Leveraging transformers and large language models with antimicrobial prescribing data to predict sources of infection for electronic health record studies2024 · 2 citations
  2. 2Large Language Model-assisted text mining reveals bacterial pathogen diversity2025
  3. 3Transformers and large language models are efficient feature extractors for electronic health record studies2024
  4. 4Harnessing Large Language Models to Advance Microbiome Research: From Sequence Analysis to Clinical Applications2025
  5. 5Using Large Language Models for Microbiome Findings Reports in Laboratory Diagnostics2024 · 1 citations