[2604.02477] Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs

Computer Science > Computer Vision and Pattern Recognition arXiv:2604.02477 (cs) [Submitted on 2 Apr 2026] Title:Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs Authors:Onur Selim Kilic, Yeti Z. Gurbuz, Cem O. Yaldiz, Afra Nawar, Etrit Haxholli, Ogul Can, Eli Waxman View a PDF of the paper titled Guideline2Graph: Profile-Aware Multimodal Parsing for Executable Clinical Decision Graphs, by Onur Selim Kilic and 6 other authors View PDF HTML (experimental) Abstract: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 inp...