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Related Concept Videos

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Related Experiment Video

Updated: Jan 11, 2026

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
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Enhancing predictive modeling for respiratory support with LLM-driven guideline adherence.

Xiaolei Lu1, Michael Miller2, Alex K Pearce2

  • 1Department of Biomedical Informatics, University of California, San Diego, La Jolla, CA, USA.

Critical Care (London, England)
|November 14, 2025
PubMed
Summary
This summary is machine-generated.

Integrating a large language model (LLM) with a deep counterfactual model improved respiratory support recommendations for intensive care unit (ICU) patients. Concordance with LLM-enhanced guidelines reduced invasive mechanical ventilation (IMV) rates and improved patient outcomes.

Keywords:
Causal inferenceGuideline adherenceHigh-flow nasal cannulaIndividualized treatment effectLarge language modelsNoninvasive ventilation

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Area of Science:

  • Critical Care Medicine
  • Artificial Intelligence in Healthcare
  • Respiratory Therapy

Background:

  • Selection of optimal respiratory support (high-flow nasal cannula vs. noninvasive ventilation) for ICU patients at risk of invasive mechanical ventilation is unclear.
  • Previous deep counterfactual models (RepFlow-CFR) lacked interpretability and guideline alignment for clinical adoption.
  • This study developed a clinical guideline-driven LLM to enhance deep counterfactual model recommendations for NIV versus HFNC.

Purpose of the Study:

  • To develop and integrate a clinical guideline-driven LLM to enhance deep counterfactual model recommendations for NIV versus HFNC.
  • To improve interpretability and guideline adherence of AI-driven treatment recommendations.
  • To assess the impact of LLM-enhanced recommendations on patient outcomes and clinical practice.

Main Methods:

  • Enhanced RepFlow-CFR by incorporating a large language model (Claude 3.5 Sonnet) for guideline adherence and explainable recommendations.
  • Configured LLM in a HIPAA-compliant AWS environment, prompted with structured patient data, clinical notes, and guideline criteria.
  • Compared LLM-enhanced recommendations with actual treatment decisions, evaluating invasive mechanical ventilation and mortality/hospice rates. Conducted a 20-case chart review for clinical validity and safety.

Main Results:

  • Treatments concordant with LLM-enhanced recommendations were associated with significantly lower invasive mechanical ventilation (IMV) rates (24.47% vs. 52.94%).
  • Concordance with LLM recommendations reduced mortality or hospice discharge (odds ratio 0.670, p=0.046).
  • In a chart review, 95% of LLM recommendations aligned with guidelines, and physicians agreed with 65% of final recommendations; 2/20 cases had potentially severe harm errors.

Conclusions:

  • Integrating LLMs for guideline enforcement enhances interpretability and clinical alignment of counterfactual models for respiratory support.
  • This hybrid framework improves concordance with real-world practice and shows potential for improved patient outcomes.
  • Future work will focus on refining contraindication detection and expanding validation through prospective clinical trials.