Automated monitoring provides a continuous stream of data that enable real-time tracking of patient state, but reliable projection of trajectory is challenged by the sparseness of data and variation in patterns. We seek to address both the challenges of device utilization and patient risk stratification.
The project aims to reduce the number of unforeseen deteriorations of patients in medical emergency departments. The project’s approach is looking at how data from the information systems that hospitals already use today can be utilised in novel ways through machine learning based models. Ideally, the models will be able to span the diversity of patients and medical device utilisation.
The project builds on the system Patient Deterioration Warning System (PDWS) which was developed in a previous project. The PDWS is currently integrated into the emergency department’s monitoring systems.
- Emergency Department, OUH
- Department of Clinical Research, OUH
- The Maersk Mc-Kinney Moller Institute, SDU
- Department of Regional Health Research
- Centre for Innovative Medical Technology (CIMT)