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