Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach

Easton, Jonathan F., Stephens, Christopher and Angelova, Maia 2014, Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach, Computers in biology and medicine, vol. 54, pp. 199-210, doi: 10.1016/j.compbiomed.2014.09.003.

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Title Risk factors and prediction of very short term versus short/intermediate term post-stroke mortality: a data mining approach
Author(s) Easton, Jonathan F.
Stephens, Christopher
Angelova, MaiaORCID iD for Angelova, Maia orcid.org/0000-0002-0931-0916
Journal name Computers in biology and medicine
Volume number 54
Start page 199
End page 210
Total pages 12
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2014-11
ISSN 0010-4825
1879-0534
Keyword(s) Data mining
Medical relevance
Mortality
Naïve Bayes analysis
Prediction
Risk factors
Stroke
Adult
Aged
Aged, 80 and over
Computer Simulation
Data Interpretation, Statistical
Female
Humans
Incidence
Longitudinal Studies
Male
Middle Aged
Models, Statistical
Pattern Recognition, Automated
Prognosis
Proportional Hazards Models
Reproducibility of Results
Sensitivity and Specificity
Survival Analysis
United Kingdom
Language eng
DOI 10.1016/j.compbiomed.2014.09.003
Field of Research 080109 Pattern Recognition and Data Mining
010202 Biological Mathematics
08 Information And Computing Sciences
11 Medical And Health Sciences
17 Psychology And Cognitive Sciences
Socio Economic Objective 920104 Diabetes
HERDC Research category C1.1 Refereed article in a scholarly journal
Copyright notice ©2014, Crown Copyright
Persistent URL http://hdl.handle.net/10536/DRO/DU:30092099

Document type: Journal Article
Collection: School of Engineering
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