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CARE REGIO – Machine Learning meets Care

Universal use through deep learning

 
Computer Science Interdisciplinary

Project description

The project is part of the CARE REGIO joint project. As part of the sub-project "Digitisation of the care transition report (PÜB)", the student project is pursuing the application of deep learning techniques to advance the standardisation of PÜBs.

 

The aim of the CARE REGIO joint project is to develop sustainable concepts for digitally supported care. The aim is to significantly reduce the burden on carers and family carers and to support those in need of care in their independence.

One of the aims of this project is to digitise patient transfer reports (PÜB for short). These are the reports that are used when patients are transferred between different care facilities. These reports contain all the information about the patient so that the receiving institution receives all the important data about the patient. Until now, these reports have been kept manually and handed over in paper form with the patient. In order to simplify this process and eliminate sources of error, this process is now to be digitalised.

Various deep learning techniques are to be used to make the approach as universally applicable as possible. The aim is to read the data from the PÜBs and transfer it to the PIO standard format.

The "nursing information object transfer sheet" (PIO) developed by mio42 GmbH on behalf of the KBV is intended to be able to map data from PÜBs in a digital and standardised format. This standard format will be used to store patient data in the electronic patient file (ePA) and will be legally binding from 2023.

 

Supervisor:

  • Prof. Dr.-Ing. Alexandra Teynor
  • Matthias Regner
  • Sabahudin Balic

Cooperation partner:

  • CARE REGIO

Students:

  • Daniel Glöttner
  • Dominik Koziol
  • Fabian Kopf
  • Julian Schanz
  • Matthias Moser
  • Maximilian Schunck
  • Philipp Schwab