High data quality is becoming increasingly critical to today’s medical research. After identifying respective data quality rules, data quality checks can be implemented relatively easily on structured data. However, Pathology reports are predominantly narrative reports. Thus, these reports, comprising microscopy, histology and diagnosis provide very few structured elements. Hence, consistency checking of the sections of a report is mainly performed manually. Recent Large Language Models provide the potential to automate this process.
Thesis Type |
|
Status |
Running |
Presentation room |
Seminar room I5 6202 |
Supervisor(s) |
Stefan Decker |
Advisor(s) |
Yongli Mou |
Contact |
mou@dbis.rwth-aachen.de |
Goals:
- Literature review: Comprehensive exploration of existing Large Language Models in the
Domain of German Medical texts - Workflow development: Propose a comprehensive workflow for consistency checking of
German Pathology report texts
If you are interested in this thesis, do not hesitate to contact us via mou@dbis.rwth-aachen.de
Knowledge in Deep Learning (Large Language Models and Graph Neural
Networks) and Knowledge Graphs
Programming language – Python