# A Hybrid Framework with Large Language Models for Rare Disease Phenotyping

arXiv: https://arxiv.org/abs/2405.10440 · HTML: https://mlsanity.com/paper/2405.10440/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 40 references that are in the map.
- First posted on arXiv 16 May 2024.

## Builds on (most cited first, 11 of 11)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease prediction](https://mlsanity.com/paper/2005.12833/) (2020)
- [BEHRT: Transformer for Electronic Health Records](https://mlsanity.com/paper/1907.09538/) (2019)
- [Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity](https://mlsanity.com/paper/2310.07521/) (2023)
- [Identifying and Extracting Rare Disease Phenotypes with Large Language Models](https://mlsanity.com/paper/2306.12656/) (2023)
- [Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotyping](https://mlsanity.com/paper/2312.06457/) (2023)
- [MedCAT -- Medical Concept Annotation Tool](https://mlsanity.com/paper/1912.10166/) (2019)
- [Large Language Models Vote: Prompting for Rare Disease Identification](https://mlsanity.com/paper/2308.12890/) (2023)
- [Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notes](https://mlsanity.com/paper/2205.05656/) (2022)

## Built on by (most cited first, 7 of 7)

- [Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Records](https://mlsanity.com/paper/2504.00053/) (2025)
- [Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentation](https://mlsanity.com/paper/2604.27014/) (2026)
- [Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseases](https://mlsanity.com/paper/2505.17065/) (2025)
- [A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosis](https://mlsanity.com/paper/2507.08529/) (2025)
- [RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Records](https://mlsanity.com/paper/2507.15867/) (2025)
- [Leveraging Large Language Models for Rare Disease Named Entity Recognition](https://mlsanity.com/paper/2508.09323/) (2025)
- [An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language models](https://mlsanity.com/paper/2602.20324/) (2026)

## Abstract (arXiv)

Rare diseases pose significant challenges in diagnosis and treatment due to their low prevalence and heterogeneous clinical presentations. Unstructured clinical notes contain valuable information for identifying rare diseases, but manual curation is time-consuming and prone to subjectivity. This study aims to develop a hybrid approach combining dictionary-based natural language processing (NLP) tools with large language models (LLMs) to improve rare disease identification from unstructured clinical reports. We propose a novel hybrid framework that integrates the Orphanet Rare Disease Ontology (ORDO) and the Unified Medical Language System (UMLS) to create a comprehensive rare disease vocabulary. The proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. Notably, the approach uncovers a significant number of potential rare disease cases not documented in structured diagnostic records, highlighting its ability to identify previously unrecognized patients.