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

• Tipo: Artigo em Livro de Atas de Conferência Internacional
• Data: 2017

Autores

Gelatti, GJ (Autor) Outra; de Carvalho, APCPLF (Autor) Outra; Pedro Pereira Rodrigues (Autor) FMUP

Ata de Conferência Internacional

Título: CBMS

Páginas: 354-355

Indexação

ISI Web of Knowledge - 2 Citações

Scopus - 2 Citações

Outras Informações

ID Authenticus: 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 2