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

Developing a computable phenotype for glioblastoma.

Neuro-oncology - 3 Jun 2024

Yan Sandra, Melnick Kaitlyn, He Xing, Lyu Tianchen, Moor Rachel S F, Still Megan E H, Mitchell Duane A, Shenkman Elizabeth A, Wang Han, Guo Yi, Bian Jiang, Ghiaseddin Ashley P

Abstract excerpt

BACKGROUND: Glioblastoma is the most common malignant brain tumor, and thus it is important to be able to identify patients with this diagnosis for population studies. However, this can be challenging as diagnostic codes are nonspecific. The aim of this study was to create a computable phenotype (CP) for glioblastoma multiforme (GBM) from structured and unstructured data to identify patients with this condition...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Brain Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Electronic Health Records

Share to Glioblastoma

Share to Phenotype

Source

Open in PubMed Open full text

Identifiers

PMID: 38141226
DOI: 10.1093/neuonc/noad249
PMCID: PMC11145437

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

Developing a computable phenotype for glioblastoma.PMID 38141226Read ↗

Develop and Validate a Computable Phenotype for the Identification of Alzheimer’s Disease Patients Using Electronic Health Record DataSemantic proximity Read ↗

Selective prediction for extracting unstructured clinical dataSemantic proximity Read ↗

A general framework for developing computable clinical phenotype algorithms.Semantic proximity Read ↗

A Systematic Process for Assessing Fitness-for-Purpose of Health Outcomes for Computable Phenotyping with Electronic Health Record DataSemantic proximity Read ↗

Brain as Language: Neuro-Semantic Modeling of Glioblastoma RadiogenomicsSemantic proximity Read ↗

Develop and validate a computable phenotype for identifying alcohol-use disorder patients using structure and unstructured EHR data.Semantic proximity Read ↗

Beyond Phecodes: leveraging PheMAP to identify patients lacking diagnosis codes in electronic health records.Semantic proximity Read ↗

MIMIC-IV Phenotype Atlas (MIPA): A Publicly Available Dataset for EHR PhenotypingSemantic proximity Read ↗

Phecoder: semantic retrieval for auditing and expanding ICD-based phenotypes in EHR biobanksSemantic proximity Read ↗

Computable phenotype for diagnostic error: developing the data schema for application of symptom-disease pair analysis of diagnostic error (SPADE).Semantic proximity Read ↗

Application of concise machine learning to construct accurate and interpretable EHR computable phenotypesSemantic proximity Read ↗

Trends and opportunities in computable clinical phenotyping: A scoping review.Semantic proximity Read ↗

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