function [risk,prediction]=physionet2012(time,param,value) % [risk,prediction]=physionet2012(time,param,value) % % Sample Submission for the PhysioNet 2012 Challenge. Variables are: % % time - (Nx1 Cell Array) Cell array containing time of measurement % param - (Nx1 Cell Array) Cell array containing type (category) of % measurement % value - (Nx1 Double Array) Double array containing value of measurement % % % risk - (Scalar) estimate of the risk of the patient dying in hospital % prediction - (Logical)Binary classification if the patient is going to die % in the hospital (1 - Died, 0 - Survived) % % Example: % [risk,prediction]=physionet2012(time,param,value) % % Written by Ikaro Silva, 2012 % % Version 1.1 % %The sample submission calculates the in hospital death %probability based on Bayesian Rule conditioned on SAPS_SCORE %and PDF fitting based on training Set A. % % P(dying | SAPS_SCORE) = P(dying)*P(SAPS_SCORE|Dying)/ % (P(dying)*P(SAPS_SCORE|dying) + P(living)*P(SAPS_SCORE|living)) % %Calculate likelihood, P(SAPS_SCORE|Dying), based polynomial fitting of the CDF %of the training data A conditioned on those that died (values of the fit %are hardcoded below) % % saps_died=SAPS_SCORE(ihd==1); %ihd is logical vector where 1 = in hospital death % MX_SAPS=4*14; % [Ndied,xx]=hist(saps_died,[0:MX_SAPS]); % md=nanmean(saps_died); % st_d=nanstd(saps_died) % pdf_died=normpdf(xx,md,st_d); % plot(xx,Ndied./sum(Ndied));hold on;grid on;plot(xx,pdf_died,'r') %Check fit % % Repeat to get conditional probability on those that lived using Extreme % Value Distribution % saps_alive=SAPS_SCORE(ihd==0); %ihd is logical vector where 1 = in hospital death % [Nalive,xx]=hist(saps_alive,[0:MX_SAPS]); % parmhat = evfit(saps_alive(~isnan(saps_alive))); % pdf_alive=evpdf(xx,parmhat(1),parmhat(2)); % figure % %This fitting is not very good, but for the purpose of an example will do % plot(xx,Nalive./sum(Nalive));hold on;grid on;plot(xx,pdf_alive,'r') TH=0.2038; %Threshold for classifying the patient as non-survivor %Determined through maximization of the min(PPV,Sensitivity) on %training Set A %Calculate SAPS SCORE truncated=1; %Attempt to calculate SAPS even if some variables are missing [SAPS_SCORE]=saps_score(time,param,value,truncated); %Hard-coded values based on the analysis commented above md=017.644699140401148; st_d=4.677514903313259; likelihood_saps_dying=normpdf(SAPS_SCORE,md,st_d); parmhat =[15.927414293093566 4.930958270510013]; likelihood_saps_living=evpdf(SAPS_SCORE,parmhat(1),parmhat(2)); pdf_dying= 0.1385;%Based on set A %Calcuate the Posteriori Probability given the SAPS_SCORE posteriori_num= (pdf_dying*likelihood_saps_dying); posteriori_den= (pdf_dying*likelihood_saps_dying + (1-pdf_dying)*likelihood_saps_living); risk=posteriori_num/posteriori_den; prediction=risk>=TH;