Programpunkter

This presentation is about our work on a brain tumor detection using clustering methods. The ultimate goal is to use this process in a Digital Twin prototype

Learn about Vulcan, an HL7 FHIR Accelerator that is developing interoperability standards in clinical research. During the workshop, we'll provide an overview of HL7, HL7 FHIR, the Vulcan Program, recent EuroVulcan event in Paris and HL7 FHIR activities in Europe. Workshop participants will discuss how to continue to develop & implement clinical research standards in Europe.

The integration of design thinking into medical informatics education is essential for development of innovative and effective health information technologies in our work domain. Design thinking is a problem-solving approach which focuses on understanding complex user needs and creating innovative solutions befitting those complex needs. It is a widely used approach for developing user-centered products and services, and distils creative methodologies to improve usability and effectiveness of health information systems.
This workshop discusses use cases for design thinking in medical informatics education, provides an overview of the lessons learned of incorporating design thinking into the curriculum, and will challenge the audience to experiment with two of the design thinking phases. The workshop also outlines the implications of design thinking for medical informatics educators, students and our work field, and provides suggestions for incorporating design thinking into existing medical informatics programs.

The workshop is cancelled. 

An interactive workshop to get familiar with the Social Return on Investment (SROI) methodology. The attendees will learn the basics about the SROI methodology and how this methodology is used within the European RE-SAMPLE project to ensure impact of the digital innovation for every stakeholder (societal impact). Next to this, the attendees will, by means of a group discussion, form a first opinion about the SROI methodology.

We benchmark the influence of four types of noise annotated in the public dataset PTB-XL on the accuracy of a Deep Learning-based method for atrial fibrillation detection in 12-lead ECGs. For this we use the noise metadata provided by human experts as well as a quantitative signal-to-noise ratio for assigning a signal quality to each ECG and analyze the respective DL accuracy. We conclude that the issue of processing noisy electrocardiography data can be addressed successfully by Deep Learning methods.

The aim of this presentation and paper is to identify the underlying obstacles and provide recommendations to promote the dissemination of competency-based learning objectives catalogs for curricular development in health data and information sciences.