Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

RankVar: machine learning-based variant ranking and reinterpretation for rare genetic diseases.

Genome medicine·2026

Same author

Improving Retrieval-Augmented Generation without Taxonomy-based Error Categorization.

Proceedings of the conference. Association for Computational Linguistics. Meeting·2026

Same author

Evaluating unsupervised and rule-based phenotyping methods versus administrative code counts for systemic sclerosis identification.

Seminars in arthritis and rheumatism·2026

Same author

Hard to Halt: Automation Bias in Agent-Driven Sequencing Prior Authorization Workflows.

medRxiv : the preprint server for health sciences·2026

Same author

Unsupervised characterization of 100,272 EHR patients identifies high-risk groups and comorbidities linked to premature aging.

NPJ digital medicine·2026

Same author

TimeX: Phenotype Onset Extraction from Clinical Narratives.

npj health systems·2026

Search research articles

Related Experiment Video

Updated: Jul 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Enhancing Phenotype Recognition in Clinical Notes Using Large Language Models: PhenoBCBERT and PhenoGPT.

Jingye Yang^1,2, Cong Liu³, Wendy Deng¹

¹Raymond G. Perelman Center for Cellular and Molecular Therapeutics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA.

|November 21, 2023

Summary

New AI models, PhenoBCBERT and PhenoGPT, improve recognition of genetic disease phenotypes in clinical notes by expanding Human Phenotype Ontology (HPO) terms, aiding disease analysis.

Keywords:

Human Phenotype Ontology clinical notes electronic health records named entity recognition transformer

More Related Videos

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Related Experiment Videos

Last Updated: Jul 10, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts

Published on: September 20, 2018

Area of Science:

Computational biology
Medical informatics
Genetics

Background:

Human Phenotype Ontology (HPO) provides standardized phenotype vocabulary for genetic diseases.
Existing phenotype recognition tools struggle with comprehensive term capture, limiting clinical note analysis.

Approach:

Developed PhenoBCBERT and PhenoGPT using large language models (LLMs) to automate phenotype term detection.
Expanded HPO vocabularies by identifying terms beyond current HPO coverage.
Compared LLM-based models against PhenoTagger for performance evaluation.

Key Points:

PhenoBCBERT and PhenoGPT identify a broader range of phenotype concepts, including novel ones.
Models demonstrated strong performance in biomedical literature case studies.
Evaluated architectural and accuracy differences between BERT-based and GPT-based LLMs.

Conclusions:

LLM-based models significantly enhance automated phenotype detection in clinical texts.
Improved phenotype recognition facilitates more robust downstream analyses of human diseases.
These models offer a powerful tool for advancing genetic disease research and clinical applications.