Principles of database and knowledge-base systems, Vol. I
Principles of database and knowledge-base systems, Vol. I
Discrete-time signal processing
Discrete-time signal processing
Stream Query Processing for Healthcare Bio-sensor Applications
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Design, implementation, and evaluation of the linear road bnchmark on the stream processing core
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
An Infrastructure of Stream Data Mining, Fusion and Management for Monitored Patients
CBMS '06 Proceedings of the 19th IEEE Symposium on Computer-Based Medical Systems
Advanced Methods And Tools for ECG Data Analysis
Advanced Methods And Tools for ECG Data Analysis
SPC: a distributed, scalable platform for data mining
Proceedings of the 4th international workshop on Data mining standards, services and platforms
Century: Automated Aspects of Patient Care
RTCSA '07 Proceedings of the 13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Locomotion Monitoring Using Body Sensor Networks
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
SPADE: the system s declarative stream processing engine
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ISWC '07 Proceedings of the 2007 11th IEEE International Symposium on Wearable Computers
Real-time analysis for short-term prognosis in intensive care
IBM Journal of Research and Development
| Hi-index | 0.00 |
Advances in sensor technologies have accelerated the instrumentation of medical institutions. Today, modern intensive care units use sophisticated patient monitoring systems able to produce massive amounts of physiological streaming data. While these monitoring systems aim at improving patient care and staff productivity, they have the potential of introducing a data explosion problem. We address this problem by developing an open infrastructure upon which healthcare analytics can be built, managed, and deployed to analyze in real time physiological streaming data and turn this data into meaningful information for medical professionals. This infrastructure incorporates feature extraction and data mining functionalities for the discovery of clinical rules capable of identifying medically significant events. The system is based on a state of the art stream computing middleware. This paper presents this infrastructure from a programming model perspective. An exemplar application for arrhythmia detection is also described to illustrate its capabilities.