% MATLAB script for processing a Challenge 2012 data set with a Challenge entry
% Version 2.0 (22 March 2012)
%
% A script similar to this one will be used as part of the evaluation of
% Challenge entries written in m-code. A companion script will be used to
% perform the same function for Challenge entries written in C and other
% languages. We have provided these scripts so that Challenge participants
% can test their entries to verify that they run properly in the environment
% that will be used to test them.
%
% Each Challenge .txt file (record) contains data for one patient, in 3 columns
% (timestamp, parameter, and value). The three Challenge data sets contain
% 4000 records each.
%
% This script supplies a complete set of 4000 records, one at a time, to an
% entry, and collects its output for each record (a binary prediction of
% survival, for event 1, and an estimate of mortality risk, for event 2)
% in a summary output file.
%
% The summary output file is then scored by comparing its contents with the
% patients' known outcomes. This script includes MATLAB code that can
% calculate unofficial scores for the training data (set A), for which the
% known outcomes are provided to participants. Entries will be ranked using
% official scores obtained using the same methods, but based on testing with
% sets B and C, for which the outcomes are not provided to participants.
%
% If your entry is written in m-code, it must be in the form of a function
% named physionet2012, with this signature:
% [risk,survival]=physionet2012(tm,category,val);
% See the sample entry at http://physionet.org/challenge/2012/physionet2012.m
% for descriptions of the input and output variables.
%
% To use this script to obtain an unofficial score for your entry on set A:
% 1. Download these files from http://physionet.org/challenge/2012/ and save
% them in your MATLAB working directory:
% genresults.m (this file)
% lemeshow.m (function needed to calculate Event 2 score)
% set-a.zip or set-a.tar.gz (zip archive or tarball of set A files)
% Outcomes-a.txt (known outcomes for set A)
% 2. Unzip set-a.zip (or unpack set-a.tar.gz), creating a subdirectory within
% your working directory called 'set-a'. When you have completed this
% step, the set-a directory should contain 4000 individual .txt files.
% 3. Save a copy of your entry (physionet2012.m) in your working directory.
% 4. The next few lines are MATLAB code that clears any previously set
% variables, and sets the name of the directory containing the input
% data, the name for the summary output file, and the name of the file
% containing the known outcomes. Change them if necessary.
clear all;close all;clc
set_name='set-b';
fname_out='Outputs-b.txt';
% results='Outcomes-a.txt';
results=[];
% 5. Start MATLAB and type
% Main_Challenge
% The fname_out file will be generated in the
% current directory.
%
% You can also use this script to run your entry on test set B, but it will
% not be able to calculate scores in this case, since the outcomes are provided
% for set A only. To do this, download and unzip/unpack set B into a 'set-b'
% subdirectory as you did for set A, and uncomment the next three lines:
%
% set_name='set-b';
% fname_out='Outputs-b.txt'
% results=[];
%
% Note that if you run a test on either set A or B more than once, you should
% delete (or rename) your summary output file (named in fname_out), since this
% code appends new outputs to any existing output file rather than starting
% over.
cur_dir=pwd;
fdel='/';
if(ispc)
fdel='\';
end
data_dir=[cur_dir fdel set_name fdel];
cd(data_dir)
records=dir('*.txt');
cd(cur_dir)
I=length(records);
DATA=zeros(I,3) + NaN;
display(['Processing records ...'])
% Each Challenge .txt file (record) contains data for one patient, in 3 columns
% (timestamp, parameter, and value). During each iteration of the loop below,
% the contents of a single record are loaded into arrays named tm,
% category, and val. Each data set (A, B, and C) contains 4000 records.
header={'tm','category','val'};
data = cell(I,4);
for i=1:I
%=== Load in data
record_id=records(i).name(1:end-4);
fname=[data_dir record_id '.txt'];
fid_in=fopen(fname,'r');
tempCell = textscan(fid_in,'%f:%f %s %f','delimiter',',',...
'EmptyValue',NaN,'HeaderLines',1);
fclose(fid_in);
%=== time is converted from 00:00 -> # minutes since admission
data(i,:) = ...
[str2double(record_id), {tempCell{1}*60+tempCell{2}},tempCell(3),tempCell(4)];
end
% The contents of all .txt input file are now ready to be given to your
% physionet2012 function for analysis in the next line:
tic;
[risk,survival]=physionet2012(data);
fprintf('%g: %3.2f \n',size(data,1),toc);
DATA(:,1)=cell2mat(data(:,1));
DATA(:,2)=risk;
DATA(:,3)=survival;
%%
% Open fname_out and append to any previous contents
fid_out=fopen(fname_out,'a');
for i=1:I
record_id=records(i).name(1:end-4);
% The outputs of the analysis are now available in the risk (event 2)
% and survival (event 1) variables output by the function.
if(~mod(i,500))
display(['Processed: ' num2str(i) ' records out of ' num2str(I)])
end
% Format the output in 3 columns (RecordID, survival, risk).
del_ind=strfind(fname,fdel);
if(isempty(del_ind))
del_ind=0;
end
count=fprintf(fid_out,'%s,%u,%f\n',fname(del_ind(end)+1:end-4),survival(i),risk(i));
if(~count)
warning(['**No data written to output file:' fname_out ' from input file: ' fname])
end
end
fclose(fid_out);
display(['*** All records processed'])
%
% % If the known outcomes are available, the code below calculates unoffical
% % event 1 and event 2 scores. Scores based on set A are not used to rank
% % entries, since all participants have been given the correct answers!
%
% if(~isempty(results))
%
% % The file of known outcomes contains six columns. The Challenge goal is
% % to predict the sixth column, IHD (in-hospital death).
% variables={'record_id_res','SAPS','SOFA','LOS','Survival','IHD'};
% fid_result=fopen(results,'r');
% C=textscan(fid_result,'%f %f %f %f %f %f','delimiter', ',','HeaderLines',1);
% fclose(fid_result);
% for n=1:length(variables)
% eval([variables{n} '=C{:,n};'])
% end
%
% % Calculate sensitivity (Se) and positive predictivity (PPV)
% TP=sum(DATA(IHD==1,3));
% FN=sum(~DATA(IHD==1,3));
% FP=sum(DATA(IHD==0,3));
% Se=TP/(TP+FN);
% PPV=TP/(TP+FP);
%
% show=1; % if show is 1, the decile graph will be displayed by lemeshow()
% H=lemeshow([IHD DATA(:,2)],show);
%
% % Use the title of figure to display the results
% title(['H= ' num2str(H) ' Se= ' num2str(Se) ' PPV= ' num2str(PPV)])
%
% % The event 1 score is the smaller of Se and PPV.
% score1 = min(Se, PPV);
% display(['Unofficial Event 1 score: ' num2str(score1)]);
%
% % The event 2 score is the Hosmer-Lemeshow statistic (H).
% display(['Unofficial Event 2 score: ' num2str(H)]);
% end