CAI-X

AI project

Identifying patients at risk (PDWS)

Patient monitoring in emergency departments typically includes basic physiological vital signs as well as assessments of pain and consciousness. 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.

Aim

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. 

Participants

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)

Project period

Start: 2019 

End: 2022

Contact

Thomas Schmidt

Assistant Professor

The Maersk Mc-Kinney Moller Institute

(+45) 24 23 74 34
[email protected]

Amin Naemi

Amin Naemi

PhD student

The Maersk Mc-Kinney Moller Institute

(+45) 65 50 74 66
[email protected]

Kristian Kidholm

Professor, Head of Research

Centre for Innovative Medical Technology

(+45) 30 58 64 77
[email protected]

Turkis firkant (dekoration)

Annmarie Lassen

Professor, Chief Psysician

Emergency Department

(+45) 65 41 50 48
[email protected]