ArticlePatterns (New York, N.Y.)2024
Enhancing phenotype recognition in clinical notes using large language models: PhenoBCBERT and PhenoGPT.
Article in Patterns (New York, N.Y.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 42 papers, 1 of them a synthesis that pooled it.
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Who cites it
42 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review on the generative AI applications in human medical genetics.Frontiers in genetics · 2025Pooled it
- PhenoSS: phenotype semantic similarity-based approach for rare disease prediction and patient clustering.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- PubMind: literature-based genetic variant extraction and functional annotation using large language models.Nature communications · 2026Article
- Multimodal integrated knowledge transfer to large language models through preference optimization with biomedical applications.Patterns (New York, N.Y.) · 2026Article
- RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.Genome medicine · 2026Article
- AutoPCR: automated phenotype concept recognition by prompting.Bioinformatics (Oxford, England) · 2026Article
- Is one run enough? Reproducibility of flagship large language models across temperature and reasoning settings in biomedical text processing.Journal of the American Medical Informatics Association : JAMIA · 2026Article
- A generative approach for semantic auditing of electronic health records.NPJ digital medicine · 2026Article
- Note-Level Phenotyping of Multiple-Sclerosis Notes by a Large Language Model Achieves near Human-Level Agreement.Journal of clinical medicine · 2026Article
- Interpretable fine-tuned large language models facilitate making genetic test decisions for rare diseases.NPJ digital medicine · 2026Article
- PhenoSS: Phenotype semantic similarity-based approach for rare disease prediction and patient clustering.medRxiv : the preprint server for health sciences · 2026Article
- Integration of LLMs and VLMs in plant stress phenotyping: From trait recognition to decision support.Plant phenomics (Washington, D.C.) · 2026Review
- Artificial Intelligence agents for biological research: a survey.Briefings in bioinformatics · 2026Article
- TimeX: Phenotype Onset Extraction from Clinical Narratives.npj health systems · 2026Article
- Generative Models and Sentence Transformers for the Recognition and Normalization of Continuous and Discontinuous Phenotype Mentions: Model Development and Evaluation.JMIR medical informatics · 2025Article
- Improving automated deep phenotyping through large language models using retrieval-augmented generation.Genome medicine · 2025Article
- Large Language Models for Psychiatric Phenotype Extraction from Electronic Health Records.medRxiv : the preprint server for health sciences · 2025Article
- Performance of ChatGPT-4o and Four Open-Source Large Language Models in Generating Diagnoses Based on China's Rare Disease Catalog: Comparative Study.Journal of medical Internet research · 2025Article
- Do LLMs Surpass Encoders for Biomedical NER?Proceedings. IEEE International Conference on Healthcare Informatics · 2025Article
- Applying artificial intelligence to rare diseases: a literature review highlighting lessons from Fabry disease.Orphanet journal of rare diseases · 2025Review
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
To enhance phenotype recognition in clinical notes of genetic diseases, we developed two models-PhenoBCBERT and PhenoGPT-for expanding the vocabularies of Human Phenotype Ontology (HPO) terms. While HPO offers a standardized vocabulary for phenotypes, existing tools often fail to capture the full scope of phenotypes due to limitations from traditional heuristic or rule-based approaches. Our models leverage large language models to automate the detection of phenotype terms, including those not in the current HPO. We compare these models with PhenoTagger, another HPO recognition tool, and found that our models identify a wider range of phenotype concepts, including previously uncharacterized ones. Our models also show strong performance in case studies on biomedical literature. We evaluate the strengths and weaknesses of BERT- and GPT-based models in aspects such as architecture and accuracy. Overall, our models enhance automated phenotype detection from clinical texts, improving downstream analyses on human diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.