function [ pred ] = pniLouis(data)
%PNILOUIS Louis's initial entry - severity of illness score
% [ pred ] = pniLouis(data) calculates a mortality prediction for each
% each row (observation/subject) in data
%
% The score uses the following variables:
% urine, platelets, BUN, creatinine, PaFi ratio, PaO2, PaCO2, pH,
% heart_rate, temperature, BP, and age.
%
% Inputs:
% data - Cell array of data.
% Column 1 - Subject IDs
% Column 2 - Time stamp vectors for each subject
% Column 3 - Feature name vectors for each subject
% Column 4 - Data value vectors for each subject
%
% Outputs:
% pred - Column vector of predictions
%
% Example
% %=== Load data in
% load('data_processed_cell.mat');
%
% %=== Calculate score
% [ score ] = pniAndrew(data);
%
% See also PNMAIN PNPREPROCESSDATA
% References:
% Physionet Challenge 2012
% http://physionet.org/challenge/2012/
%
% Copyright 2012 Alistair Johnson
% $LastChangedBy: alistair $
% $LastChangedDate: 2012-04-25 01:26:50 +0100 (Wed, 25 Apr 2012) $
% $Revision: 344 $
% Originally written on GLNXA64 by Alistair Johnson, 15-Apr-2012 14:40:13
% Contact: alistairewj@gmail.com
mdl=load('ModelC.mat');
[ d1,d2 ] = pniExtractFeaturesC(data);
D1var = [true(1,size(d1,2)),false(1,size(d2,2))];
X = [d1,d2]; mu=mdl.mu; sigma=mdl.sigma;
for v=1:size(X,2) % normalize
if sigma(v)~=0
X(:,v) = (X(:,v)-mu(v))/sigma(v);
end
end
% Impute 0 for NaNs
X(isnan(X)) = 0;
[ pred ] = pniClassifyC(X,mdl,D1var);
end