Large Language Model-Based Clinical Decision Support for Antibiotic Selection and Dose Recommendation in Hospitalized...

Area of Science:

• Medical Informatics
• Computational Medicine
• Pharmacology

Background:

• Pneumonia treatment requires balancing antibiotic efficacy, safety, and resistance, often relying on clinician experience.
• Current large language models (LLMs) face challenges like hallucinations and constraint adherence for clinical decision support.
• There's a need for reliable AI tools to guide antibiotic selection and dosing in hospitalized pneumonia patients.

Purpose of the Study:

• To develop and externally validate a constrained LLM-based clinical decision support pipeline.
• To enhance antibiotic selection and dose recommendations for hospitalized pneumonia patients.
• To improve the reliability and interpretability of AI-driven antibiotic guidance.

Main Methods:

• A multicenter retrospective study involving 331 hospitalized pneumonia patients from two Chinese hospitals.
• Development of a pipeline integrating dual-branch retrieval (case-based and knowledge graph) and clinician-defined rules.
• Evaluation of LLMs (DeepSeek-V3, GLM-4.6, GPT-4o) using F1-score and Jaccard accuracy on internal and external validation sets.

Main Results:

• The full pipeline with DeepSeek-V3 achieved high F1-scores (0.8110 internal, 0.8605 external) for antibiotic selection.
• Joint antibiotic selection and dosing recommendations also showed strong performance (0.7538 internal, 0.8503 external F1-scores).
• The system provided traceable evidence and rule triggers, enhancing transparency for clinicians.

Conclusions:

• A constrained, retrieval-augmented LLM pipeline significantly improved consistency and interpretability in antibiotic recommendations.
• The system demonstrated preliminary cross-site generalizability, suggesting potential for wider clinical application.
• This approach offers a promising advancement for AI-assisted antibiotic stewardship in pneumonia care.