Access Restriction

Author Ongenae, F. ♦ Dhaene, T. ♦ De Turck, F. ♦ Benoit, D. ♦ Decruyenaere, J.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Ontologies ♦ Time series analysis ♦ Pathology ♦ Databases ♦ Feature extraction ♦ Probabilistic logic ♦ Monitoring
Abstract Medical time series contain important information about the condition of a patient. However, due to the large amount of data and the staff shortage, it is difficult for physicians to monitor these time series for trends that suggest a relevant clinical detoriation due to a complication or new pathology. This paper proposes a framework that supports physicians in detecting patterns in time series. It has three main tasks. First, the time-dependent data is gathered from heterogeneous sources and the semantics are made explicit by using an ontology. Second, Machine Learning techniques detect trends in the semantic time series data that indicate that a patient has a particular pathology. However, computerized classification techniques are not 100% accurate. Therefore, the third task consists of adding the pathology classification to the ontology with an associated probability and notifying the physician if necessary. The framework was evaluated with an ICU use case, namely detecting sepsis. Sepsis is the number one cause of death in the ICU.
Description Author affiliation: Department of Intensive Care, Ghent University Hospital, De Pintelaan 185, 9000 Ghent, Belgium (Benoit, D.; Decruyenaere, J.) || Department of Information Technology, Ghent University - IBBT, Gaston Crommenlaan 8 bus 201, 9050 Ghent, Belgium (Ongenae, F.; Dhaene, T.; De Turck, F.)
ISBN 9781424491674
ISSN 10637125
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-10-12
Publisher Place Australia
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781424491681
Size (in Bytes) 1.31 MB
Page Count 6
Starting Page 389
Ending Page 394

Source: IEEE Xplore Digital Library