# Utilizing Large Language Models for Zero-Shot Medical Ontology Extension from Clinical Notes

arXiv: https://arxiv.org/abs/2511.16548 · HTML: https://mlsanity.com/paper/2511.16548/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 25 references that are in the map.
- First posted on arXiv 20 Nov 2025.

## Builds on (most cited first, 9 of 9)

- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Stanza: A Python Natural Language Processing Toolkit for Many Human Languages](https://mlsanity.com/paper/2003.07082/) (2020)
- [Self-Alignment Pretraining for Biomedical Entity Representations](https://mlsanity.com/paper/2010.11784/) (2020)
- [PromptLink: Leveraging Large Language Models for Cross-Source Biomedical Concept Linking](https://mlsanity.com/paper/2405.07500/) (2024)
- [KERAP: A Knowledge-Enhanced Reasoning Approach for Accurate Zero-shot Diagnosis Prediction Using Multi-agent LLMs](https://mlsanity.com/paper/2507.02773/) (2025)
- [HypKG: Hypergraph-based Knowledge Graph Contextualization for Precision Healthcare](https://mlsanity.com/paper/2507.19726/) (2025)
- [Automated and Explainable Ontology Extension Based on Deep Learning: A Case Study in the Chemical Domain](https://mlsanity.com/paper/2109.09202/) (2021)
- [Towards Automatic Evaluation and Selection of PHI De-identification Models via Multi-Agent Collaboration](https://mlsanity.com/paper/2510.16194/) (2025)
- [Large Language Model Empowered Privacy-Protected Framework for PHI Annotation in Clinical Notes](https://mlsanity.com/paper/2504.18569/) (2025)

## Built on by (most cited first, 4 of 4)

- [EpiQAL: Benchmarking Large Language Models in Epidemiological Question Answering and Reasoning](https://mlsanity.com/paper/2601.03471/) (2026)
- [Closing Reasoning Gaps in Clinical Agents with Differential Reasoning Learning](https://mlsanity.com/paper/2602.09945/) (2026)
- [MIRAGE: Knowledge Graph-Guided Cross-Cohort MRI Synthesis for Alzheimer's Disease Prediction](https://mlsanity.com/paper/2603.02434/) (2026)
- [Enhanced Atrial Fibrillation Prediction in ESUS Patients with Hypergraph-based Pre-training](https://mlsanity.com/paper/2603.13297/) (2026)

## Abstract (arXiv)

Integrating novel medical concepts and relationships into existing ontologies can significantly enhance their coverage and utility for both biomedical research and clinical applications. Clinical notes, as unstructured documents rich with detailed patient observations, offer valuable context-specific insights and represent a promising yet underutilized source for ontology extension. Despite this potential, directly leveraging clinical notes for ontology extension remains largely unexplored. To address this gap, we propose CLOZE, a novel framework that uses large language models (LLMs) to automatically extract medical entities from clinical notes and integrate them into hierarchical medical ontologies. By capitalizing on the strong language understanding and extensive biomedical knowledge of pre-trained LLMs, CLOZE effectively identifies disease-related concepts and captures complex hierarchical relationships. The zero-shot framework requires no additional training or labeled data, making it a cost-efficient solution. Furthermore, CLOZE ensures patient privacy through automated removal of protected health information (PHI). Experimental results demonstrate that CLOZE provides an accurate, scalable, and privacy-preserving ontology extension framework, with strong potential to support a wide range of downstream applications in biomedical research and clinical informatics.