Guideline2Graph：面向可执行临床决策图的多模态解析
Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs
AI总结：
本文提出一种多模态解析方法，通过拓扑感知分块、接口约束分块生成和溯源保留的全局聚合，将完整指南证据转换为可执行临床决策支持图。实验表明，该方法在前列腺指南基准上显著提升了精度和召回率。
摘要：
临床实践指南是长篇多模态文档，其分支推荐难以转化为可执行的临床决策支持（CDS）。单次解析常破坏跨页连续性。最近的LLM/VLM提取器大多是局部或文本中心的，未能明确界定章节接口并无法将跨页控制流整合为一个连贯的决策图。我们提出一种分解优先的流程，通过拓扑感知分块、接口约束分块生成和溯源保留的全局聚合，将完整指南证据转换为可执行的临床决策图。该流程不依赖单次生成，而是通过显式的入口/终端接口和语义去重，保留跨页连续性，同时保持诱导的控制流可审计且结构一致。我们在一个裁定的前列腺指南基准上进行了评估，使用匹配的输入和相同的基础VLM模型。在完整的合并图上，我们的方法将边和三元组的精度/召回率从现有模型的19.6%/16.1%提升到69.0%/87.5%，同时节点召回率从78.1%提升到93.8%。这些结果支持了该基准上的分解优先、可审计的指南到CDS转换，而当前证据仍局限于一个裁定的前列腺指南，激励更广泛的多指南验证。
英文摘要：
Clinical practice guidelines are long, multimodal documents whose branching recommendations are difficult to convert into executable clinical decision support (CDS), and one-shot parsing often breaks cross-page continuity. Recent LLM/VLM extractors are mostly local or text-centric, under-specifying section interfaces and failing to consolidate cross-page control flow across full documents into one coherent decision graph. We present a decomposition-first pipeline that converts full-guideline evidence into an executable clinical decision graph through topology-aware chunking, interface-constrained chunk graph generation, and provenance-preserving global aggregation. Rather than relying on single-pass generation, the pipeline uses explicit entry/terminal interfaces and semantic deduplication to preserve cross-page continuity while keeping the induced control flow auditable and structurally consistent. We evaluate on an adjudicated prostate-guideline benchmark with matched inputs and the same underlying VLM backbone across compared methods. On the complete merged graph, our approach improves edge and triplet precision/recall from $19.6\%/16.1\%$ in existing models to $69.0\%/87.5\%$, while node recall rises from $78.1\%$ to $93.8\%$. These results support decomposition-first, auditable guideline-to-CDS conversion on this benchmark, while current evidence remains limited to one adjudicated prostate guideline and motivates broader multi-guideline validation.
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