Predicting Mortality of ICU Patients: The PhysioNet/Computing in Cardiology Challenge 2012 1.0.0

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 (param) 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)
% 




TH=0.2700; 
TH_SAPS=0.3900;     
           
           
           
B=[ 1.4037238316e+00;
    -8.1833389095e-02;
    -2.4939330155e-02;
    -1.5119424507e-02;
    2.7833454606e-02;
    -1.4908319274e-02;
    1.2317990562e-02;
    1.0654354558e-02;
    5.6335820205e-02;
    2.0570191510e-01;
    -2.7707893188e-01;
    4.0909316076e-04;
    -1.7088353456e-04;
    -5.4632240876e-03;
     5.3569006748e-03;
    1.2035782695e-02;
    -4.1221677189e-02];    


B_saps=[ 
    4.8341800475e+00;
    2.1514309096e-01;
   -2.3846361213e-01;
];
   
I=1;
i=1;

[ALL_CATEGORIES,time_series_names,descriptors]=get_param_names();
num_params=length(ALL_CATEGORIES);
num_ts_params=length(time_series_names);
num_descriptors=length(descriptors);


MEAN_DATA_24=zeros(I,num_ts_params) + NaN;
MEAN_DATA_48=zeros(I,num_ts_params) + NaN;

DESCRIPTORS=zeros(I,num_descriptors) + NaN; 

[times,values,names]=extract_param_series(time,param,value);

[ts_times,ts_values,ts_names]=get_param_subset(time_series_names,times,values,names);

[des_times,des_values,des_names]=get_param_subset(descriptors,times,values,names);
    
DESCRIPTORS(i,:)=cell2mat(des_values(:))';
    
means24=calculate_mean(ts_times,ts_values,ts_names,time_series_names,[0 24*60]);
means48=calculate_mean(ts_times,ts_values,ts_names,time_series_names,[24*60 48*60]);
MEAN_DATA_24(i,:)=means24;
MEAN_DATA_48(i,:)=means48;

  score=saps_score(time,param,value,1,[0 24]);
   if isempty(score)
        SAPS_SCORES(i)=NaN;
   else
        SAPS_SCORES(i)=score;
   end
   
   score=saps_score(time,param,value,1,[24 48]);
   if isempty(score)
        SAPS_SCORES_48(i)=NaN;
     else
        SAPS_SCORES_48(i)=score;
    end
      
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'HCO3'))-MEAN_DATA_48(:,strcmp(time_series_names,'HCO3'))];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'HCO3')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Glucose'))-MEAN_DATA_48(:,strcmp(time_series_names,'Glucose')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Glucose')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Urine'))-MEAN_DATA_48(:,strcmp(time_series_names,'Urine')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Urine')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'GCS'))-MEAN_DATA_48(:,strcmp(time_series_names,'GCS')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'GCS')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Na'))-MEAN_DATA_48(:,strcmp(time_series_names,'Na')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'Na')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'HR'))-MEAN_DATA_48(:,strcmp(time_series_names,'HR')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'HR')) X_Test];

X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'BUN'))-MEAN_DATA_48(:,strcmp(time_series_names,'BUN')) X_Test ];
X_Test=[MEAN_DATA_24(:,strcmp(time_series_names,'BUN')) X_Test];     
 
X_Test=[DESCRIPTORS(:,strcmp(des_names,'Age')) X_Test];

X_Test=[DESCRIPTORS(:,strcmp(des_names,'ICUType')) X_Test];

PHAT = mnrval(B,X_Test);


if isnan(PHAT(:,2))
X_trunc_saps=[SAPS_SCORES-SAPS_SCORES_48 SAPS_SCORES ];
PHAT_saps = mnrval(B_saps,X_trunc_saps);
end

if isnan(PHAT(2))
    risk=PHAT_saps(2);
    prediction=risk>TH_SAPS;
elseif PHAT(2)<0.01
    risk=0.01;
    prediction=risk>TH;
elseif PHAT(2)>0.99
    risk=0.99;
    prediction=risk>TH;
else
    risk=PHAT(2);
    prediction=risk>TH;
end