用检索增强生成技术，帮医生根据病历开药时参考相似病例。
Retrieval-Augmented Framework for LLM-Based Clinical Decision Support
- 结合病历文本和结构化数据，通过检索相似病例来生成建议。
- 在脱敏合成数据上测试，输出具有临床合理性和一致性。
- 适合希望安全使用生成式AI辅助开药的医疗机构参考。
临床决策日益复杂，电子健康记录（EHR）数据激增，为数据驱动诊疗带来机遇与挑战。本文提出一种基于大语言模型（LLM）的临床决策支持系统，帮助处方医生生成治疗建议。系统分析患者人口学、主诉、症状、诊断及治疗史等历史EHR数据，融合自然语言处理与结构化输入，生成上下文相关的推荐。该框架采用检索增强生成（RAG）管道，整合非结构化文本与编码数据，支持LLM推理。系统通过检索本地或联邦来源中特征相近的既往病例，辅助而非取代医生判断。我们阐述了表示对齐与生成策略等技术组件。初步评估使用去标识化与合成临床数据集，检验输出的临床合理性与一致性。早期结果表明，在适当约束与严格验证下，生成式AI工具可在处方流程中提供有价值的支持。本工作是将生成式AI融入真实世界临床决策的初步探索，强调透明性、安全性与对现有实践的对齐。
原文摘要 · Abstract (English)
The increasing complexity of clinical decision-making, alongside the rapid expansion of electronic health records (EHR), presents both opportunities and challenges for delivering data-informed care. This paper proposes a clinical decision support system powered by Large Language Models (LLMs) to assist prescribing clinicians. The system generates therapeutic suggestions by analyzing historical EHR data, including patient demographics, presenting complaints, clinical symptoms, diagnostic information, and treatment histories. The framework integrates natural language processing with structured clinical inputs to produce contextually relevant recommendations. Rather than replacing clinician judgment, it is designed to augment decision-making by retrieving and synthesizing precedent cases with comparable characteristics, drawing on local datasets or federated sources where applicable. At its core, the system employs a retrieval-augmented generation (RAG) pipeline that harmonizes unstructured narratives and codified data to support LLM-based inference. We outline the system's technical components, including representation representation alignment and generation strategies. Preliminary evaluations, conducted with de-identified and synthetic clinical datasets, examine the clinical plausibility and consistency of the model's outputs. Early findings suggest that LLM-based tools may provide valuable decision support in prescribing workflows when appropriately constrained and rigorously validated. This work represents an initial step toward integration of generative AI into real-world clinical decision-making with an emphasis on transparency, safety, and alignment with established practices.
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