Linked Life Data

20351817

Source:http://linkedlifedata.com/resource/pubmed/id/20351817

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2010-3-30
pubmed:abstractText
The increasing availability of complex temporal clinical records collected today has prompted the development of new methods that extend classical machine learning and data mining approaches to time series data. In this work, we develop a new framework for classifying the patient's time-series data based on temporal abstractions. The proposed STF-Mine algorithm automatically mines discriminative temporal abstraction patterns from the data and uses them to learn a classification model. We apply our approach to predict HPF4 test orders from electronic patient health records. This test is often prescribed when the patient is at the risk of Heparin induced thrombocytopenia (HIT). Our results demonstrate the benefit of our approach in learning accurate time series classifiers, a key step in the development of intelligent clinical monitoring systems.
pubmed:grant
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
1942-597X
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
2009
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
29-33
pubmed:dateRevised
2011-5-16
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
A temporal abstraction framework for classifying clinical temporal data.
pubmed:affiliation
Department of Computer Science, University of Pittsburgh, PA, USA.
pubmed:publicationType
Journal Article