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

arXiv: https://arxiv.org/abs/2606.28353 · HTML: https://mlsanity.com/paper/2606.28353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 30 references that are in the map.
- First posted on arXiv 6 Jun 2026.

## Builds on (most cited first, 12 of 12)

- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Dense Passage Retrieval for Open-Domain Question Answering](https://mlsanity.com/paper/2004.04906/) (2020)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Passage Re-ranking with BERT](https://mlsanity.com/paper/1901.04085/) (2019)
- [Improving Biomedical Pretrained Language Models with Knowledge](https://mlsanity.com/paper/2104.10344/) (2021)
- [Engineering Knowledge Graph from Patent Database](https://mlsanity.com/paper/2106.06739/) (2021)
- [LLM-RankFusion: Mitigating Intrinsic Inconsistency in LLM-based Ranking](https://mlsanity.com/paper/2406.00231/) (2024)
- [Zero-shot Neural Transfer for Cross-lingual Entity Linking](https://mlsanity.com/paper/1811.04154/) (2018)
- [PubMed knowledge graph 2.0: Connecting papers, patents, and clinical trials in biomedical science](https://mlsanity.com/paper/2410.07969/) (2024)
- [Improving Zero-Shot Entity Retrieval through Effective Dense Representations](https://mlsanity.com/paper/2103.04156/) (2021)
- [Disentangled Modeling of Domain and Relevance for Adaptable Dense Retrieval](https://mlsanity.com/paper/2208.05753/) (2022)
- [Structure and Semantics Preserving Document Representations](https://mlsanity.com/paper/2201.03720/) (2022)

## Abstract (arXiv)

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.