mlsanityData to 7 Oct 2026

A Hybrid Framework with Large Language Models for Rare Disease Phenotyping

arXiv 2405.10440 ↗First posted 16 May 2024cs.CLcited by 7

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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.

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

Builds on

11 of 40 references.

References · 11

  1. 2023GPT-4 Technical Reportcited by 18,132
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2019BioBERT: a pre-trained biomedical language representation model for biomedical text miningcited by 1,935
  4. 2020Med-BERT: pre-trained contextualized embeddings on large-scale structured electronic health records for disease predictioncited by 368
  5. 2019BEHRT: Transformer for Electronic Health Recordscited by 258
  6. 2023Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificitycited by 204
  7. 2023Identifying and Extracting Rare Disease Phenotypes with Large Language Modelscited by 18
  8. 2023Large Language Models with Retrieval-Augmented Generation for Zero-Shot Disease Phenotypingcited by 11
  9. 2019MedCAT -- Medical Concept Annotation Toolcited by 10
  10. 2023Large Language Models Vote: Prompting for Rare Disease Identificationcited by 9
  11. 2022Ontology-Driven and Weakly Supervised Rare Disease Identification from Clinical Notescited by 7

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 5; 2026: 2.

7 papers.

  1. 2025Integrating Large Language Models with Human Expertise for Disease Detection in Electronic Health Recordscited by 1
  2. 2026Fidelity, Diversity, and Privacy: A Multi-Dimensional LLM Evaluation for Clinical Data Augmentationcited by 1
  3. 2025Decoding Rarity: Large Language Models in the Diagnosis of Rare Diseasescited by 0
  4. 2025A Multi-granularity Concept Sparse Activation and Hierarchical Knowledge Graph Fusion Framework for Rare Disease Diagnosiscited by 0
  5. 2025RDMA: Cost Effective Agent-Driven Rare Disease Mining from Electronic Health Recordscited by 0
  6. 2025Leveraging Large Language Models for Rare Disease Named Entity Recognitioncited by 0
  7. 2026An artificial intelligence framework for end-to-end rare disease phenotyping from clinical notes using large language modelscited by 0