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Identifying patients at risk (PDWS)

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

Project period

Start: 2019 
End: 2022

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. 


Thomas Schmidt

Thomas Schmidt

Associate Professor

Maersk Mc-Kinney Moeller Institute

(+45) 24 23 74 34
Amin Naemi

Amin Naemi

PhD student

Maersk Mc-Kinney Moller Institute

(+45) 65 50 74 66
Kristian Kidholm

Kristian Kidholm

Professor, Head of Research

Centre for Innovative Medical Technology

(+45) 6541 7960

Annmarie Lassen

Professor, Chief Physician

The Emergency Department

(+45) 65 41 50 48