arXiv:2606.28353cs.IRcs.AI2026-06ACL

将医疗器械批准信息与专利关联,助力监管与知识产权分析。

From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceability

论文配图:From Regulatory Approvals to Patents: Cross-Domain Linking for Cardiovascular Device Traceability
图 1 · 摘自论文原文
  • 构建领域本体与多信号候选生成,缓解临床与技术术语的语义鸿沟。
  • 在434个心血管设备上实现91.6%召回率,噪声降低50.9%。
  • 适合医疗监管、并购分析及技术演进研究者使用。

将美国食品药品监督管理局(FDA)批准的医疗器械与其在美国专利商标局(USPTO)的专利关联,可支持召回根因分析、并购驱动的知识产权发现以及技术演进路径追踪等关键应用。然而,由于医疗文件侧重临床结果,专利则描述技术机制,两者存在严重语义差异,导致词汇重叠极少,该跨域实体链接任务长期未被探索。本文以心血管设备为高影响力代表性领域,构建包含434个设备、69.8万项专利和585对专家验证的高质量配对基准。提出Bridge-MedDevKG框架:首先构建领域本体MedDevOnto,通过三层UMLS标准化锚定设备概念;其次融合公司关联、语义相似度与本体加权实体重叠生成候选;最后采用异构重排序,结合多信号评分与XGBoost分类处理难负例。实验显示,在黄金标准上达到91.6%的保守召回率,噪声减少50.9%,显著优于同类大模型基线。最终构建的MedDevKG提供680万条高置信链接,为医疗各领域的监管-知识产权整合奠定可扩展基础。

原文摘要 · Abstract (English)

Linking FDA-approved medical devices to their underlying United States Patent and Trademark Office (USPTO) patents enables critical applications such as recall root-cause analysis, M&A-driven IP discovery, and technology trajectory mapping. However, this cross-domain entity linking task remains unexplored due to severe *semantic gaps*: FDA documents focus on clinical outcomes, while patents describe technical mechanisms, yielding minimal lexical overlap. We formalize medical device-patent linking as a challenging cross-domain entity linking problem characterized by label scarcity and domain shifts. Using cardiovascular devices as a high-impact, representative domain featuring diverse technologies, high recall rates, and abundant disclosures, we construct a benchmark with 434 devices, 698K patents, and 585 high-fidelity expert-verified pairs. To address these challenges, we propose Bridge-MedDevKG, a coarse-to-fine framework that integrates (1) **MedDevOnto**, a domain-specific ontology that anchors device concepts via three-tier UMLS normalization; (2) **Multi-signal candidate generation** fusing company affiliation, semantic similarity, and ontology-weighted entity overlap; and (3) **Heterogeneous reranking** with multi-signal scoring and XGBoost classification on hard negatives. Our approach achieves a conservative lower-bound recall of 91.6% on the gold standard with 50.9% noise reduction, substantially outperforming LLM baselines under comparable evaluation. The resulting MedDevKG provides 6.8M high-confidence links, laying a scalable foundation for regulatory-IP integration across medical specialties.

医疗器械专利挖掘跨域链接监管科技

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