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References (27)

[1]
Contextual Augmentation for Entity Linking using Large Language Models
2025Daniel Vollmers, Hamada M. Zahera et al.
[2]
Enhancing data quality in medical concept normalization through large language models
2025Haihua Chen, Ruochi Li et al.
[3]
Guiding Large Language Models for Biomedical Entity Linking via Restrictive and Contrastive Decoding
2025Zhenxi Lin, Ziheng Zhang et al.
[4]
LLM as Entity Disambiguator for Biomedical Entity-Linking
2025Christophe Ye, Cassie S. Mitchell
[5]
Improving Phenotyping of Patients With Immune-Mediated Inflammatory Diseases Through Automated Processing of Discharge Summaries: Multicenter Cohort Study
2024Adam REMAKI, Jacques Ung et al.
[6]
Learning from Negative Samples in Biomedical Generative Entity Linking
2024Chanhwi Kim, Hyunjae Kim et al.
[7]
The Llama 3 Herd of Models
2024Abhimanyu Dubey, Abhinav Jauhri et al.
[8]
Improving biomedical entity linking for complex entity mentions with LLM-based text simplification
2024Florian Borchert, Ignacio Llorca et al.
[9]
Instructed Language Models with Retrievers Are Powerful Entity Linkers
2023Zilin Xiao, Ming Gong et al.
[10]
Exploiting Asymmetry for Synthetic Training Data Generation: SynthIE and the Case of Information Extraction
2023Martin Josifoski, Marija Sakota et al.
[11]
Overview of MedProcNER Task on Medical Procedure Detection and Entity Linking at BioASQ 2023
2023Salvador Lima-López, Eulàlia Farré-Maduell et al.
[12]
An overview of biomedical entity linking throughout the years
2022E. French, Bridget Mcinnes
[13]
Generative Biomedical Entity Linking via Knowledge Base-Guided Pre-training and Synonyms-Aware Fine-tuning
2022Hongyi Yuan, Zheng Yuan et al.
[14]
Overview of DisTEMIST at BioASQ: Automatic detection and normalization of diseases from clinical texts: results, methods, evaluation and multilingual resources
2022Antonio Miranda-Escalada, Luis Gasco et al.
[15]
Knowledge-Rich Self-Supervision for Biomedical Entity Linking
2021Sheng Zhang, Hao Cheng et al.
[16]
Entity Linking via Explicit Mention-Mention Coreference Modeling
2021Dhruv Agarwal, Rico Angell et al.
[17]
Learning Domain-Specialised Representations for Cross-Lingual Biomedical Entity Linking
2021Fangyu Liu, Ivan Vulic et al.
[18]
Ambiguity in medical concept normalization: An analysis of types and coverage in electronic health record datasets
2020Denis R. Newman-Griffis, Guy Divita et al.
[19]
CODER: Knowledge-infused cross-lingual medical term embedding for term normalization
2020Zheng Yuan, Zhengyun Zhao et al.
[20]
Self-Alignment Pretraining for Biomedical Entity Representations
2020Fangyu Liu, Ehsan Shareghi et al.

Showing 20 of 27 references

Founder's Pitch

"Revolutionize biomedical entity linking using synthetic augmentation to significantly reduce data annotation costs."

Biomedical AIScore: 9View PDF ↗

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4/4 signals

10

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4/4 signals

10

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Why It Matters

This research provides a robust solution for the expensive and labor-intensive process of annotating biomedical data, which is critical for improving healthcare AI systems' performance and scalability.

Product Angle

The solution can be offered as a cloud-based API service, allowing organizations to seamlessly incorporate advanced biomedical entity linking capabilities into existing systems to enhance data processing and clinical research outcomes.

Disruption

SynCABEL's framework could replace existing manual annotation workflows and less efficient entity linking systems, streamlining data processing in biomedical research and application.

Product Opportunity

The product targets healthcare institutions, R&D companies, and clinical trial organizations. They pay for more efficient and accurate entity linking, reducing costs associated with data annotation and improving data utility in biomedical research.

Use Case Idea

Develop a subscription-based platform for healthcare providers and biomedical companies, enabling them to integrate this enhanced entity linking to improve their data annotation processes and data-driven research outcomes.

Science

SynCABEL uses large language models to synthetically generate rich contextual data for candidate concepts in biomedical databases, reducing the need for human-annotated training data. It achieves superior performance across multilingual biomedical entity linking benchmarks with a more efficient annotation process.

Method & Eval

The paper evaluates SynCABEL using three benchmarks: MedMentions, QUAERO, and SPACCC, demonstrating state-of-the-art results. It also introduces an LLM-as-a-judge protocol that provides a more qualitative assessment of predictions' clinical validity.

Caveats

The reliance on synthetic data might introduce biases if not carefully managed, and the actual clinical deployment needs rigorous validation to ensure that replacing human annotation does not miss critical nuances.

Author Intelligence

Author Name Unknown