Anomaly detection through temporal abstractions on intensive care data: position paper

• Type: Article in International Conference Proceedings Book
• Date: 2017

Authors

Gelatti, GJ (Author) Other; de Carvalho, APCPLF (Author) Other; Pedro Pereira Rodrigues (Author) FMUP

Conference proceedings International

Title: CBMS

Pages: 354-355

Indexing

ISI Web of Knowledge - 2 Citations

Scopus - 2 Citations

Other information

Authenticus ID: P-00N-6A9

DOI: 10.1109/cbms.2017.146

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 2