@inProceedings{
 title = {Data preprocessing and mortality prediction: The Physionet/CinC 2012 challenge revisited},
 type = {inProceedings},
 year = {2014},
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 keywords = {AUROC,Box-Cox outlier rejection technique,Data models,Data preprocessing,Feature extraction,Heart rate,ICU patient mortality prediction,Physionet-CinC 2012 challenge,Predictive models,Support vector machines,Training,area under the receiver operating characteristic,cardiology,complex nonlinear machine learning algorithms,computing in cardiology,data mining,intensive care unit,learning (artificial intelligence),machine learning classifiers,medical computing,prognostic model,random forests,regression analysis,regression model performance,sophisticated machine learning algorithms,standard pre-processing methods},
 pages = {157-160},
 volume = {41},
 websites = {http://ieeexplore.ieee.org/articleDetails.jsp?arnumber=7043003},
 publisher = {IEEE},
 id = {95f7f0b7-e3e0-3ebf-9d8e-fb803970deca},
 created = {2016-03-29T18:26:46.000Z},
 accessed = {2015-06-02},
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 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
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 citation_key = {Johnson2014},
 language = {English},
 bibtype = {inProceedings},
 author = {Johnson, Alistair Ew and Kramer, Andrew /A/. and Clifford, Gari D.},
 booktitle = {Computing in Cardiology}
}

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