Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

An evaluation of GPT models for phenotype concept recognition.

BMC medical informatics and decision making - 31 Jan 2024

Groza Tudor, Caufield Harry, Gration Dylan, Baynam Gareth, Haendel Melissa A, Robinson Peter N, Mungall Christopher J, Reese Justin T

Abstract excerpt

OBJECTIVE: Clinical deep phenotyping and phenotype annotation play a critical role in both the diagnosis of patients with rare disorders as well as in building computationally-tractable knowledge in the rare disorders field. These processes rely on using ontology concepts, often from the Human Phenotype Ontology, in conjunction with a phenotype concept recognition task (supported usually by machine learning...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to GPT gene
Share to Language

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Machine Learning

Share to Phenotype

Share to Rare Diseases

Source

Open in PubMed Open full text

Identifiers

PMID: 38297371
DOI: 10.1186/s12911-024-02439-w
PMCID: PMC10829255

Genes

GPT

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026. Manifest publication-graph-cohort-2026-09-07.1.

Source receipt: pubmed, retrieved 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

An evaluation of GPT models for phenotype concept recognition.PMID 38297371Read ↗

Consistent Performance of GPT-4o in Rare Disease Diagnosis Across Nine Languages and 4967 CasesSemantic proximity Read ↗

Assessing the utility of large language models for phenotype-driven gene prioritization in the diagnosis of rare genetic disease.Semantic proximity Read ↗

ChatGPT for phenotypes extraction: one model to rule them all?Semantic proximity Read ↗

Bridging clinical narratives and structured phenotypes with large language models and sentence transformers.Semantic proximity Read ↗

Improving automated deep phenotyping through large language models using retrieval-augmented generation.Semantic proximity Read ↗

FastHPOCR: pragmatic, fast, and accurate concept recognition using the human phenotype ontology.Semantic proximity Read ↗

Improving Automated Deep Phenotyping Through Large Language Models Using Retrieval Augmented GenerationSemantic proximity Read ↗

Extraction of Human Phenotype Ontology (HPO) Concepts from Clinical Notes Utilizing Large Language Models (LLM) with Model Context Protocol (MCP)Semantic proximity Read ↗

Genetic Transformer: An Innovative Large Language Model Driven Approach for Rapid and Accurate Identification of Causative Variants in Rare Genetic DiseasesSemantic proximity Read ↗

PhenoBERT: A Combined Deep Learning Method for Automated Recognition of Human Phenotype Ontology.Semantic proximity Read ↗

Fine-tuning large language models for rare disease concept normalization.Semantic proximity Read ↗

Systematic benchmarking demonstrates large language models have not reached the diagnostic accuracy of traditional rare-disease decision support toolsSemantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.