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

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System.

Pharmacoepidemiology and drug safety - 1 Jul 2026

Yang Jie, Gu Bowen, Pillai Haritha, Lii Joyce, Cronkite David, Marsolo Keith A, Desai Rishi J

Abstract excerpt

PURPOSE: Studies based on electronic health records (EHR) often rely on structured data, which may incompletely capture important clinical phenotypes in EHR notes. The purpose of this study was to assess two natural language processing (NLP) tools to extract phenotypes from unstructured EHR notes, and to evaluate the added value of integrating NLP-derived phenotypes with structured EHR data at a health system...

Read the complete abstract on PubMed

Topics

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

Share to Aged
Share to Electronic Health Records

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Share to Natural Language Processing

Share to Obesity

Share to Phenotype

Share to Prevalence

Share to Smoking

Source

Identifiers

PMID: 42301002
DOI: 10.1002/pds.70415

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 9 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare System.PMID 42301002Read ↗

Using Natural Language Processing of Clinical Notes to Supplement Structured Electronic Health Record Data for Phenotyping Smoking and Obesity in a Healthcare SystemSemantic proximity Read ↗

Development and Application of Natural Language Processing on Unstructured Data in Hypertension: A Scoping ReviewSemantic proximity Read ↗

Extracting Social Determinants of Health from Electronic Health Records: Development and Comparison of Rule-Based and Large Language Model MethodsSemantic proximity Read ↗

Scalable information extraction from free text electronic health records using large language modelsSemantic 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 ↗

Natural Language Processing for assessing multimorbidity: A systematic reviewSemantic proximity Read ↗

Scalable information extraction from free text electronic health records using large language modelsSemantic proximity Read ↗

MedSDoH: A Rule-Based System for Extracting Social Determinants of Health from Multi-site EHRs Based on the OHNLP FrameworkSemantic proximity Read ↗

Augmented Intelligence with Natural Language Processing Applied to Electronic Health Records is Useful for Identifying Patients with Non-Alcoholic Fatty Liver Disease at Risk for Disease ProgressionSemantic proximity Read ↗

Toward a Computable Phenotype for Determining Eligibility of Lung Cancer Screening Using Electronic Health Records.Semantic proximity Read ↗

Scalable Incident Detection via Natural Language Processing and Probabilistic Language ModelsSemantic proximity Read ↗

Large Language Models for Social Determinants of Health Information Extraction from Clinical Notes – A Generalizable Approach across InstitutionsSemantic proximity Read ↗

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