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

Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.

Journal of biomedical informatics - 1 Sept 2024

Lu Yiwen, Tong Jiayi, Chubak Jessica, Lumley Thomas, Hubbard Rebecca A, Xu Hua, Chen Yong

Abstract excerpt

OBJECTIVES: It has become increasingly common for multiple computable phenotypes from electronic health records (EHR) to be developed for a given phenotype. However, EHR-based association studies often focus on a single phenotype. In this paper, we develop a method aiming to simultaneously make use of multiple EHR-derived phenotypes for reduction of bias due to phenotyping error and improved efficiency of...

Read the complete abstract on PubMed

Topics

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

Share to Algorithms
Share to Bias
Share to Colonic Neoplasms

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Share to Computer Simulation

Share to Electronic Health Records

Share to Phenotype

Share to Risk Factors

Source

Identifiers

PMID: 39004110
DOI: 10.1016/j.jbi.2024.104690

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 3 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data.PMID 39004110Read ↗

A cost-effective chart review sampling design to account for phenotyping error in electronic health records (EHR) data.Semantic proximity Read ↗

Reducing Bias Due to Outcome Misclassification for Epidemiologic Studies Using EHR-derived Probabilistic Phenotypes.Semantic proximity Read ↗

Evaluating the impact of modeling choices on the performance of integrated genetic and clinical modelsSemantic proximity Read ↗

Development and Validation of Phenotype Classifiers across Multiple Sites in the Observational Health Sciences and Informatics (OHDSI) NetworkSemantic proximity Read ↗

Leveraging undecided cases in chart-reviewed phenotypes to enhance EHR-based association studies.Semantic proximity Read ↗

Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassificationSemantic proximity Read ↗

An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies.Semantic proximity Read ↗

Improving the power of genetic association tests with imperfect phenotype derived from electronic medical records.Semantic proximity Read ↗

Evaluating the Bias, type I error and statistical power of the prior Knowledge-Guided integrated likelihood estimation (PIE) for bias reduction in EHR based association studies.Semantic proximity Read ↗

Assessing Phenotype Definitions for Algorithmic Fairness.Semantic proximity Read ↗

Implicit bias of encoded variables: frameworks for addressing structured bias in EHR-GWAS data.Semantic proximity Read ↗

Evaluating the effect of multiple genetic risk score models on colorectal cancer risk prediction.Semantic proximity Read ↗

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