Related Experiment Video
Updated: Sep 23, 2025
Intravascular Ultrasound Image-Based Finite Element Modeling Approach for Quantifying In Vivo Mechanical Properties of Human Coronary Artery
Published on: December 6, 2024
Towards a FHIR-Based Data Model for Coronary Angiography Observations
Florian Holweg1, Stephan Achenbach2, Noemi Deppenwiese3
1Chair of Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany.
Insights
This study develops prognostic models to predict myocardial infarction risk using coronary angiograms and clinical data. The research harmonizes data for federated learning, improving cardiovascular disease prediction.
Area of Science:
- Cardiology
- Medical Informatics
- Machine Learning
Background:
- Coronary heart disease is a leading global cause of death.
- Predictive models for myocardial infarction are crucial for early intervention.
- Standardizing cardiac data is essential for advanced analytics.
Purpose of the Study:
- To develop prognostic models for predicting spontaneous myocardial infarctions.
- To integrate clinical parameters with invasive coronary angiogram data.
- To establish a harmonized data model for cross-hospital federated learning.
Main Methods:
- Utilized over 30,000 coronary angiograms from Erlangen University Hospital.
- Developed a mapping for coronary angiography data based on HL7® FHIR®.
- Mapped data to ICD-10 and SNOMED CT for standardization.
- Extracted 15 patient characteristics to create HL7® FHIR® resources.
Main Results:
- A finalized coronary angiography information model was designed.
- A robust mapping approach to clinical terminologies was established.
- Data harmonization strategy for federated learning was defined.
Conclusions:
- The developed information model and mapping facilitate data integration for prognostic modeling.
- This approach supports future federated machine learning initiatives in cardiology.
- The standardized data can improve the prediction accuracy of myocardial infarctions.
Abstract:
Coronary heart disease is among the most frequent causes of death globally. Thus, our research project aims to develop prognostic models, to predict the risk of spontaneous myocardial infarctions based on a combination of clinical parameters and image data sets (invasive coronary angiograms). To train such models we use data from more than 30,000 coronary angiograms acquired at the cardiology department of Erlangen University Hospital. To linking such proprietary data with additional clinical parameters and to harmonize it for future cross-hospital federated machine learning approaches we defined a mapping for coronary angiography based on the symptom/ clinical phenotype HL7® FHIR® module of the German medical informatics initiative. In this paper we describe the final design of the coronary angiography information model and our mapping approach to ICD-10 and SNOMED CT. From the database we use a subset of 15 required values patient characteristics to create the HL7® FHIR® resource.
More Related Videos
Related Concept Videos
01:27Coronary Artery Disease V: Interprofessional Care
01:30Acute Coronary Syndrome III: Diagnostic Studies
01:30Coronary Artery Disease I: Introduction
01:28Imaging Studies for Cardiovascular System V: CT
01:13Radiological Investigation III: Pulmonary Angiogram and PET Scan
Pulmonary Angiogram
A Pulmonary Angiogram is an invasive procedure involving injecting a contrast medium through a catheter threaded into the pulmonary artery or the right side of the heart to visualize the pulmonary vasculature. Computed Tomography (CT) scans have mainly replaced this...
01:20Imaging Studies for Cardiovascular System III: X-Ray
Definition and Purpose
An X-ray, or radiograph, is a non-invasive method that uses ionizing radiation to take images of internal structures. It is mainly used in cardiac imaging to examine the heart, lungs, and major blood vessels, aiming to identify abnormalities in the heart's size, shape, and position, such as heart failure, congenital defects, and vascular...