DBIS

In recent years, as newer technologies have evolved around the healthcare ecosystem, more and more data have been generated.

Advanced analytics could power the data collected from numerous sources, both from healthcare institutions, or generated by individuals themselves via apps and devices, and lead to innovations in treatment and diagnosis pf diseases; improve the care given to the patient; and empower citizens to participate in the decision-making process regarding their own health and well-being. However, the sensitive nature of the health data prohibits healthcare organizations from sharing the data.

The Personal Health Train (PHT) is a novel approach, aiming to establish a distributed data analytics infrastructure enabling the (re)use of distributed healthcare data, while data owners stay in control of their own data. The main principle of the PHT is that data remain in their original location, and analytical tasks visit data sources and execute the tasks. The PHT provides a distributed, flexible approach to use data in a network of participants, incorporating the FAIR principles. It facilitates the responsible use of sensitive and/or personal data by adopting international principles and regulations.

The task of this thesis is the investigation of re-occurring pattern in the train’s code, such as input interfaces, analysis, and storage of the results.
After the formalization of these detected patterns, the goal is the development of libraries, which standardize the patterns. These libraries should be in different coding languages and should be easily extendable.

If you are interested in this thesis, do not hesitate to contact us via welten@dbis.rwth-aachen.de.
Please attach your CV and your current gradings.