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

Analysis of Population Pharmacokinetic Data

Analysis of Population Pharmacokinetic Data

Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...

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: Dec 26, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation.

Kayla Schiffer-Kane¹, Cong Liu¹, Tiffany J Callahan¹

¹Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, USA.

Journal of Biomedical Informatics

|May 22, 2024

Summary

This summary is machine-generated.

This study aligns Observational Medical Outcomes Partnership (OMOP) and Phenopackets data models to enhance precision medicine interoperability. A new pipeline and UMLS semantic type filtering improve real-world patient data mapping to Phenopackets, aiding clinical applications.

Keywords:

Data model Health data standards Interoperability OMOP-CDM Phenopackets schema Phenotyping

More Related Videos

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Published on: July 12, 2013

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Related Experiment Videos

Last Updated: Dec 26, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Imaging of Biological Tissues by Desorption Electrospray Ionization Mass Spectrometry

Published on: July 12, 2013

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Near Infrared Optical Projection Tomography for Assessments of β-cell Mass Distribution in Diabetes Research

Published on: January 12, 2013

Area of Science:

Biomedical Informatics
Translational Research
Precision Medicine

Background:

Interoperability is crucial for precision medicine and translational research.
Existing data models like OMOP and Phenopackets present challenges for seamless data exchange.
Phenopackets facilitates multimodal patient data storage and analysis.

Purpose of the Study:

To promote interoperability by aligning OMOP and Phenopackets data models.
To develop and evaluate a data transformation process for mapping OMOP data to Phenopackets.
To assess the suitability of Phenopackets for representing real-world clinical data.

Main Methods:

Developed a data transformation process to map OMOP to Phenopackets.
Applied the transformation to 1,000 Alzheimer's disease patient records from OMOP.
Incorporated Unified Medical Language System (UMLS) semantic type filtering for ambiguous concept alignment.
Conducted domain-expert evaluation of the mapping's clinical utility.

Main Results:

Successfully mapped required entities for 1,000 Alzheimer's patients, with a 10.2% data loss due to missing OMOP values.
UMLS semantic type filtering achieved 96% agreement with clinical thinking, a significant improvement from 68% without it.
Identified unmappable Phenopacket attributes for specialty use cases not supported by OMOP.

Conclusions:

A pipeline for transforming OMOP to Phenopackets data was established.
Key considerations for data quality, validation, and format handling were identified.
UMLS semantic type filtering effectively resolves ambiguous alignments, enhancing interpretability.
This work addresses key interoperability barriers, facilitating Phenopackets' use in clinical settings.