neurosurgical-book-parser

Extracting structured knowledge from neurosurgical textbooks involves parsing PDFs or MinerU output to identify anatomical entities, surgical procedures, techniques, and complications, then mapping them into a typed knowledge graph with preserved chapter and page context for Neo4j loading.

Yes. This Skill converts neurosurgical and spine surgery textbooks into Neo4j-compatible knowledge graphs capturing anatomical structures, surgical steps with sequencing, clipping techniques, and clinical relationships while retaining chapter and page provenance for education and clinical decision support.

A neurosurgical knowledge graph uses domain-specific entity types including arteries, aneurysms, procedures, and techniques, with explicit relationship types modeling anatomical connections, surgical sequencing, complication links, and clinical outcomes extracted from textbook content.

Chapter-aware extraction retains page and chapter metadata with each extracted entity, enabling identical anatomical terms to map to different surgical scenarios and allowing graph queries filtered by textbook location and surgical context.

The parser accepts MinerU output and PDFs of neurosurgical and spine surgery textbooks, processing the extracted text to identify domain entities and relationships suitable for graph database ingestion.

Yes. The Skill produces knowledge-graph-ready output designed for batched Neo4j loading, with kaizen-based quality logging to track extraction fidelity and enable iterative refinement of the knowledge graph.