Paper Info
Title
Toward a Big Data Healthcare Analytics System: A Mathematical Modeling Perspective
Abstract
High speed physiological data produced by medical devices at intensive care units (ICUs) has all the characteristics of Big Data. The proper use and management of such data can promote the health and reduces mortality and disability rates of critical condition patients. The effective use of Big Data within ICUs has great potential to create new cloud-based health analytics solutions for disease prevention or earlier condition onset detection. The Artemis project aims to achieve the above goals in the area of neonatal intensive care units (NICU). In this paper, we proposed an analytical model for an extended version of Artemis system which is being deployed at SickKids hospital in Toronto. Using the proposed analytical model, we predict the amount of storage, memory and computation power required for Artemis. In addition, important performance metrics such as mean number of patients in the NICU, blocking probability and mean patient residence time for different configurations are obtained. Capacity planning and trade-off analysis would be more accurate and systematic by applying the proposed analytical model in this paper. Numerical results are obtained using real inputs acquired from a pilot deployment of the system at SickKids hospital.
Year
DOI
Venue
2014
10.1109/SERVICES.2014.45
SERVICES
Keywords
Field
DocType
realtime analytics, big data, analytical modeling, capacity planning, health informatics
Data mining,Software deployment,Computer science,Healthcare analytics,Capacity planning,Analytics,Health informatics,Big data,Intensive care,Database,Cloud computing
Conference
ISSN
ISBN
Citations 
2378-3818
978-1-4799-5068-3
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Hamzeh Khazaei122317.82
Carolyn McGregor24714.22
J. Mikael Eklund37718.73
Khalil El-Khatib4274.44
Anirudh Thommandram531.98
El-Khatib, K.660.76