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Anomaly Detection Through Temporal Abstractions on Intensive Care Data: Position Paper
- Source :
- CBMS
- Publication Year :
- 2017
- Publisher :
- IEEE, 2017.
-
Abstract
- A large amount of information is continuously generated in intensive health care. An analysis of these data streams can supply valuable insights to improve the monitoring of the patients. The volume, frequency and complexity of data, which come unlabeled, make their analysis a challenging task. Machine learning (ML) techniques have been successfully employed for mining data streams to extract useful knowledge for health care monitoring. It includes the detection of changes in the behavior of sensors, failures on machines or systems, and data anomalies. Anomaly (or outlier) detection is a ML task that aims to find exceptions or abnormalities in a dataset. These exceptions, in a medical context, can represent a new disease pattern, an event to be further investigated, behavior changes or potential health complications. Despite of its analysis in data streams is a challenging task, temporal abstractions techniques should help due to they deal with the management and abstraction of time based data, offering high level of visualization of each data object in its context. The aim of this paper is to review recent research in anomaly detection and temporal abstractions and discuss the application of their combination to intensive care data streams.
- Subjects :
- Computer science
Event (computing)
business.industry
Data stream mining
Context (language use)
02 engineering and technology
Machine learning
computer.software_genre
Task (project management)
Visualization
03 medical and health sciences
0302 clinical medicine
Intensive care
Outlier
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Anomaly detection
Data mining
Artificial intelligence
business
computer
030217 neurology & neurosurgery
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2017 IEEE 30th International Symposium on Computer-Based Medical Systems (CBMS)
- Accession number :
- edsair.doi...........75551b81f10bc5e89e25f03e4069f146