Real-time multidimensional temporal analysis of complex high volume physiological data streams in the neonatal intensive care unit.
The intensive care of immature preterm infants is a challenging, dynamic clinical task that is complicated because these infants frequently develop a range of comorbidities as they grow and develop after their premature birth. Earliest reliable condition onset detection is a goal within this setting and high frequency physiological analysis is showing potential new pathophysiological indicators for earlier onset detection of several conditions. To realise this, a platform for multi-stream, multi-condition, multi-feature risk scoring is required. In this paper we demonstrate our multi-stream online analytics approach for condition onset detection and demonstrate a user interface approach for patient state that can be available in real-time to support condition risk scoring.
Faculty of Business and Information Technology, University of Ontario Institute of Technology, Oshawa, ON, Canada.
Carolyn
C
McGregor
Andrew
A
James
Mike
M
Eklund
Daby
D
Sow
Maria
M
Ebling
Marion
M
Blount
D001185:Artificial Intelligence; D003199:Computer Systems; D020000:Decision Support Systems, Clinical; D003936:Diagnosis, Computer-Assisted / Q000379:methods*; D063005:Health Information Systems; D006801:Humans; D007231:Infant, Newborn; D015931:Intensive Care, Neonatal / Q000379:methods*; D008991:Monitoring, Physiologic / Q000379:methods*; D009864:Ontario; D018805:Sepsis / Q000175:diagnosis*; D012984:Software; D014584:User-Computer Interface