function [prob,died]=physionet2012(tm,category,val)
% [prob,died]=physionet2012(tm,category,val)
% Submission for the PhysioNet 2012 Challenge.
%
% Inputs:
% tm - (Nx1 Cell Array) Cell array containing time of measurement
% category- (Nx1 Cell Array) Cell array containing type (category)
% measurement
% value - (Nx1 Cell Array) Cell array containing value of measurement
%
% Outputs:
% prob - (Scalar) Probability value of the patient dying in the hospital
% died - (Logical) Binary classification if the patient is going to die
% on the hospital (1 - Died, 0 - Survived)
%
% Example
% [prob,died]=physionet2012(tm,category,val)
% Copyright 2012 Alistair Johnson
% $LastChangedBy: alistair $
% $LastChangedDate: 2012-05-29 09:01:11 -0400 (Tue, 29 May 2012) $
% $Revision: 1 $
% Originally written on GLNXA64 by Alistair Johnson, 24-Apr-2012 14:02:25
% Contact: alistairewj@gmail.com
%=== Miscellanious default values
T=0.330400; % Mortality threshold that maximizes SE/PPV
%=======================%
%=== PREPROCESS DATA ===%
%=======================%
%=== Put into expected format for function
if nargin<3
if iscell(tm) && size(tm,2)>=3
data = tm; % assume multi-observation input
else
error('Incorrect input.');
end
elseif nargin==3
% Convert time from string to numeric minutes
tm = cellfun(@(x) str2double(x(1:2)), tm)*60 + cellfun(@(x) str2double(x(4:5)),tm);
data = [{1},{tm},{category},{val}];
end
data = pnPreprocess(data,0);
%==================%
%=== LOAD MODEL ===%
%==================%
model = load('pnEntry10.mat');
%===============================%
%=== MODEL FEATURE SELECTION ===%
%===============================%
% if isfield(model,'idxRem')
% %=== Parse data for problematic features
% [X,header] = pnParseData(X,header,idxRem);
% end
%===============================%
%=== MODEL PARSING ===%
%===============================%
% if isfield(model,'T')
% T = model.T;
% end
header = model.header;
model = model.model;
%=====================%
%=== LOAD FEATURES ===%
%=====================%
[X,header2] = pnBaseFeatures(data,header);
X(X<0) = NaN;
%===================%
%=== CLASSIFY ======%
%===================%
prob = pnBRFApply(model,X);
%===================%
%=== SET OUTPUTS ===%
%===================%
died = prob >= T; % Thresholded probability to maximize PPV/Sens
end