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

Large language models facilitate the generation of electronic health record phenotyping algorithms.

Journal of the American Medical Informatics Association : JAMIA - 1 Sept 2024

Yan Chao, Ong Henry H, Grabowska Monika E, Krantz Matthew S, Su Wu-Chen, Dickson Alyson L, Peterson Josh F, Feng QiPing, Roden Dan M, Stein C Michael, Kerchberger V Eric, Malin Bradley A, Wei Wei-Qi

Abstract excerpt

OBJECTIVES: Phenotyping is a core task in observational health research utilizing electronic health records (EHRs). Developing an accurate algorithm demands substantial input from domain experts, involving extensive literature review and evidence synthesis. This burdensome process limits scalability and delays knowledge discovery. We investigate the potential for leveraging large language models (LLMs) to enhance...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Dementia

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Diabetes Mellitus, Type 2

Share to Electronic Health Records

Share to GPT gene

Share to Hypothyroidism

Share to Natural Language Processing

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 38613820
DOI: 10.1093/jamia/ocae072
PMCID: PMC11339509

Genes

GPT

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Large language models facilitate the generation of electronic health record phenotyping algorithms.PMID 38613820Read ↗

Large Language Models Facilitate the Generation of Electronic Health Record Phenotyping AlgorithmsSemantic proximity Read ↗

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

A Systematic Exploration of LLM Behavior for EHR PhenotypingSemantic proximity Read ↗

Targeted use of large language models for EHR-based computable phenotyping.Semantic proximity Read ↗

Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping AlgorithmsSemantic 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 ↗

Evaluating Large Language Models for Translating Multimodal Phenotype Documentations into Executable EHR Phenotyping AlgorithmsSemantic proximity Read ↗

Performance Characteristics of Reasoning Large Language Models for Evidence Extraction from Clinical Genomics LiteratureSemantic proximity Read ↗

Automating Clinical Phenotyping Using Natural Language Processing: An Application for Crohn’s DiseaseSemantic proximity Read ↗

Applying natural language processing and large language models to clinical notes for phenotyping and diagnosing rare diseases: a systematic review.Semantic proximity Read ↗

Evaluation of Semantic Web Technologies for Storing Computable Definitions of Electronic Health Records Phenotyping Algorithms.Semantic proximity Read ↗

Large language models improve transferability of electronic health record-based predictions across countries and coding systemsSemantic proximity Read ↗

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