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“Digital Early Warning Scoring – A Cognitive IoT based approach”

January 18, 2018 @ 3:00 pm - 4:00 pm PST

Title: “Digital Early Warning Scoring – A Cognitive IoT based approach”
Speaker: Prof. Pasi Liljeberg, University of Turku

Date and Time: Thursday, January 25, 2018 at 2:00PM-3:00PM

Location: Engineering Hall 2430


In healthcare, effective monitoring of patients plays a key role in detecting health deterioration early enough. Many signs of deterioration exist as early as 24 hours prior having a serious impact on the health of a person. As hospitalization times have to be minimized, in-home or remote early warning systems can fill the gap by allowing in-home care while having the potentially problematic conditions and their signs under surveillance and control.

Early warning score (EWS) is an approach to detect the deterioration of a patient. It is based on a fact that there are several changes in the physiological parameters prior a clinical deterioration of a patient. Currently, EWS procedure is mostly used for in-hospital clinical cases and is performed in a manual paper-based fashion. However, it is possible to build an automated EWS health monitoring system to intelligently monitor vital signs and prevent health deterioration for in-home and hospitalized patients using Internet-of-Things technologies.


Pasi Liljeberg received the MSc and PhD degrees in electronics and information technology from the University of Turku, Turku, Finland, in 1999 and 2005, respectively. He received Adjunct professorship in embedded computing architectures in 2010. Currently he is working as full professor in University of  Turku in the field of Embedded Systems and Internet of Things. At the moment his research is focused on biomedical engineering and health technology. In that context he has established and leading the Internet-of-Things for Healthcare, IoT4Health, ( http://iot4health.utu.fi [1] ) research group. Liljeberg is the author of more than 270 peer-reviewed publications.


January 18, 2018
3:00 pm - 4:00 pm PST
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