Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Chronic Obstructive Pulmonary Disease-II: Pathophysiology01:20

Chronic Obstructive Pulmonary Disease-II: Pathophysiology

5.0K
Chronic Obstructive Pulmonary Disease (COPD) pathophysiology is intricate and multifaceted, involving a complex interplay of physiological processes. Understanding these mechanisms is crucial for effectively managing and treating COPD. Here is an in-depth look at the critical elements in the pathophysiology of COPD:
Chronic Inflammation
5.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Towards interpretable prediction of recurrence risk in breast cancer using pathology foundation models.

NPJ digital medicine·2026
Same author

The Association of Long COVID and CKD: Findings from the National Clinical Cohort Collaborative.

Clinical journal of the American Society of Nephrology : CJASN·2025
Same author

Pathomics Image Analysis of Tumor Infiltrating Lymphocytes (TILs) in Colon Cancer.

Research square·2025
Same author

Reusable specimen-level inference in computational pathology.

ArXiv·2025
Same author

The association of diabetes mellitus and routinely collected patient-reported outcomes in patients with cancer. A real-world cohort study.

Cancer medicine·2024
Same author

Explainable AI for computational pathology identifies model limitations and tissue biomarkers.

ArXiv·2024
Same journal

LabSage: Structural-Semantic Decoupling for Enhanced Retrieval-Augmented Generation in Clinical Laboratories.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Evaluating Representation Embeddings from LLMs and Time-Series Foundation Models for Wearable Accelerometer-Based Health Prediction.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

ClinNoteAgents: An LLM Multi-Agent System for Predicting and Interpreting Heart Failure 30-Day Readmission from Clinical Notes.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Mapping the Storm: Linking Tornado Paths to Emergency Room Surges Through Geocoded Patient Data.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

Multi-Modal Deep Learning-Based Model to Predict Burkitt Lymphoma Recurrence.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
Same journal

A Multi-Model LLM Consensus Framework to Identify EHR-Predictable Eligibility Criteria in NSCLC Immunotherapy Trials.

AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science·2026
See all related articles

Related Experiment Video

Updated: May 5, 2026

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica
03:32

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica

Published on: April 12, 2019

6.7K

Semantic ETL into i2b2 with Eureka!

Andrew R Post1, Tahsin Krc, Himanshu Rathod

  • 1Center for Comprehensive Informatics, Emory University, Atlanta, GA.

AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science
|December 5, 2013
PubMed
Summary
This summary is machine-generated.

Eureka! Clinical Analytics simplifies clinical phenotyping from electronic health record (EHR) data. This system enables researchers to identify complex patient comorbidities and complications using robust extract, transform, and load (ETL) processes.

More Related Videos

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation
11:07

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Published on: July 6, 2019

25.7K
Noninvasive Intratracheal Lipopolysaccharide Instillation in Mice
04:10

Noninvasive Intratracheal Lipopolysaccharide Instillation in Mice

Published on: March 31, 2023

6.9K

Related Experiment Videos

Last Updated: May 5, 2026

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica
03:32

A Pleural Effusion Model in Rats by Intratracheal Instillation of Polyacrylate/Nanosilica

Published on: April 12, 2019

6.7K
Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation
11:07

Inducing Acute Lung Injury in Mice by Direct Intratracheal Lipopolysaccharide Instillation

Published on: July 6, 2019

25.7K
Noninvasive Intratracheal Lipopolysaccharide Instillation in Mice
04:10

Noninvasive Intratracheal Lipopolysaccharide Instillation in Mice

Published on: March 31, 2023

6.9K

Area of Science:

  • Health Informatics
  • Biomedical Research
  • Clinical Data Management

Background:

  • Electronic health record (EHR) data analysis is crucial for research, often requiring identification of clinical comorbidities and complications.
  • Phenotypes are frequently represented as complex patterns in data, not discrete elements, necessitating advanced data processing.
  • Researchers need accessible tools for extracting and analyzing EHR data without extensive IT support.

Purpose of the Study:

  • To develop a user-friendly system for clinical phenotyping using EHR data.
  • To create a robust and flexible extract, transform, and load (ETL) process for computing complex phenotypes.
  • To make advanced clinical data analytics accessible to researchers with limited IT resources.

Main Methods:

  • Developed Eureka! Clinical Analytics, a system for extracting data from Excel spreadsheets.
  • Implemented ETL processes to compute a wide range of common phenotypes.
  • Integrated raw and computed data into an i2b2 project.
  • Created a web-based user interface for executing and monitoring ETL processes.

Main Results:

  • Eureka! Clinical Analytics successfully extracts, transforms, and loads clinical data for phenotyping.
  • The system computes a broad set of common phenotypes.
  • A web-based interface provides accessible control over ETL processes.
  • The system is deployed institutionally and available for cloud deployment.

Conclusions:

  • Eureka! Clinical Analytics provides an accessible and robust solution for clinical phenotyping from EHR data.
  • The system empowers researchers to identify complex clinical patterns.
  • This tool facilitates advanced data analysis for biomedical research, supporting investigators with varying IT expertise.