Medical Data Integration and Graph Structures

Motivation

Medical data is complex, heterogeneous, and often fragmented across different formats and systems. To make this data usable for advanced analytics and machine learning, it needs to be carefully integrated and transformed into structured representations. Graph-based data models are particularly well-suited for this purpose, as they allow us to capture relationships, dependencies, and structures in medical information that are difficult to represent otherwise.

In our Medical Data (Privacy) related projects, we are working on methods to transform diverse medical datasets into graph structures that serve as a foundation for data analysis and decision support. These methods also include the use of machine learning to further enrich and explore the data.

Your Challenge

In your work, you will:

• Integrate and harmonize medical datasets from multiple sources
• Design and implement graph-based structures to represent medical data
• Explore approaches for data analysis and insight generation based on these structures

This project will give you research and hands-on experience at the intersection of medical informatics, graph technologies, and machine learning.

Conditions

• Working time: 7-10 hours per week
• Start: As soon as possible

What We Offer

At COMSYS, we provide a friendly and creative working atmosphere. You can expect collaboration at eye level, supported by an open-door policy and a team-oriented environment. Our student assistants benefit from dedicated student offices and regular social gatherings. To round things off, we offer drinks and snacks at near-cost price and even a billiard table for relaxing breaks.

If you are interested in this topic, please contact Marlena via e-mail.