ArticleBMC medical informatics and decision making2024
A hybrid framework with large language models for rare disease phenotyping.
Article in BMC medical informatics and decision making, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Generative large language models in medicine: a scoping review of recent methodological advances.npj health systems · 2026Review
- AI-based multimodal integration of genomics and electronic health records.Nature reviews. Genetics · 2026Review
- Persona-Driven Data Augmentation for Disease Name Recognition Across Rare and General Disease Corpora: Comparative Evaluation Study.JMIR medical informatics · 2026Article
- Optimising pain identification in resource-limited emergency departments using transfer learning and fine-tuned language models.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A Multimodal Biomedical Transformer Fusion Network for Disease-Level Rare-Disease-Inheritance Classification Using Ontology-Enriched Text, Metadata, and Gene Associations.Biomedicines · 2026Article
- Capsule-enhanced hierarchical vision transformers for rare disease classification from medical images.Scientific reports · 2026Article
- Rare-Disease Diagnosis on the ZebraMap Multimodal Case Report Dataset: A Hybrid Pipeline with Grounded Explainability.Sensors (Basel, Switzerland) · 2026Article
- Leveraging large language models for rare disease named entity recognition.PLOS digital health · 2026Article
- Harmonizing self-reported and free text medication data: a reproducible pipeline for gerontological research.BMC medical informatics and decision making · 2025Article
- Evaluating the Accuracy of the Frysian Questionnaire for Differentiation of Musculoskeletal Complaints for Triage of Musculoskeletal Diseases: Algorithm Development and Validation Study.JMIR medical informatics · 2025Article
- A scalable approach for genomic-first rare disorder detection in a healthcare-based population.American journal of human genetics · 2025Article
- An integrated approach for rare disease detection and classification in Spanish pediatric medical reports.Scientific reports · 2025Article
- Fine-tuning medical language models for enhanced long-contextual understanding and domain expertise.Quantitative imaging in medicine and surgery · 2025Article
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10 authors.
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Abstract
purposeRare 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.
methodsWe 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. SemEHR, a dictionary-based NLP tool, is employed to extract rare disease mentions from clinical notes. To refine the results and improve accuracy, we leverage various LLMs, including LLaMA3, Phi3-mini, and domain-specific models like OpenBioLLM and BioMistral. Different prompting strategies, such as zero-shot, few-shot, and knowledge-augmented generation, are explored to optimize the LLMs' performance.
resultsThe proposed hybrid approach demonstrates superior performance compared to traditional NLP systems and standalone LLMs. LLaMA3 and Phi3-mini achieve the highest F1 scores in rare disease identification. Few-shot prompting with 1-3 examples yields the best results, while knowledge-augmented generation shows limited improvement. 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.
conclusionThe hybrid approach combining dictionary-based NLP tools with LLMs shows great promise for improving rare disease identification from unstructured clinical reports. By leveraging the strengths of both techniques, the method demonstrates superior performance and the potential to uncover hidden rare disease cases. Further research is needed to address limitations related to ontology mapping and overlapping case identification, and to integrate the approach into clinical practice for early diagnosis and improved patient outcomes.
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