Research team
Data fusion and structured input and output Machine Learning techniques for automated clinical coding.
Abstract
This project will improve the state of the art in automated clinical coding by analyzing heterogeneous data sources and defining them in a semantic structure and by developing novel data fusion and machine learning techniques for structured input and output.Researcher(s)
- Promoter: Goethals Bart
- Promoter: Van den Bulcke Tim
- Co-promoter: Daelemans Walter
- Co-promoter: Luyten Leon
- Fellow: Scheurwegs Elyne
Research team(s)
Project type(s)
- Research Project
Development of an automated software platform to support and improve the quality of clinical coding.
Abstract
The goal of this project is to develop algorithms and software to improve the quality of the clinical coding process in hospitals, and to design a valorization plan. The algorithms will automatically identify coding anomalies and suggest codes using state-of-the-art machine learning techniques. We will define a business development plan, attract potential customers, and aim to attract follow-up funding.Researcher(s)
- Promoter: Van den Bulcke Tim
- Co-promoter: Goethals Bart
- Co-promoter: Luyckx Kim
- Co-promoter: Luyten Leon
- Co-promoter: Smets Koen
Research team(s)
Project type(s)
- Research Project