Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1145/1743384.1743465 | Multimedia Information Retrieval |
Keywords | Field | DocType |
middleware,feature extraction,programming model,data processing,patient monitoring,stream computing,healthcare,distributed computing,data mining,real time | Data science,Middleware,Data stream mining,Data processing,Information retrieval,Programming paradigm,Remote patient monitoring,Computer science,Stream,Feature extraction,Intensive care,Database | Conference |
Citations | PageRank | References |
6 | 0.54 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daby M. Sow | 1 | 131 | 17.69 |
| Alain Biem | 2 | 288 | 18.64 |
| Marion Blount | 3 | 127 | 14.29 |
| Maria Ebling | 4 | 109 | 18.31 |
| Olivier Verscheure | 5 | 630 | 42.88 |