Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -240,6 +240,37 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                 verif=False
         return verif
     
+    def save_features(self,concat_cols,dyn_df,cond_df,demo):
+        #create csv with the description of each feature for analysis purpose
+        df_feats = pd.DataFrame(columns=['feature','description'])
+        icd = pd.read_csv(self.mimic_path+'/hosp/d_icd_diagnoses.csv.gz',compression='gzip', header=0)
+        items= pd.read_csv(self.mimic_path+'/icu/d_items.csv.gz',compression='gzip', header=0)
+        
+        if self.encoding == 'concat':
+            feats = concat_cols.copy()
+            df_feats['feature'] = feats
+            for _, data in df_feats.iterrows():
+                txt=(items[items['itemid'] == int(data['feature'].split('_')[0])]['label']).to_string(index=False)
+                data['description']=txt+' at interval '+data['feature'].split('_')[1]
+        else:
+            feats = list(dyn_df.columns.droplevel(0))
+            for _, data in df_feats.iterrows():
+                data['description']=(items[items['itemid'] == int(data['feature'])]['label']).to_string(index=False)
+        
+        for diag in list(cond_df.columns):
+            df_feats.loc[len(df_feats)] = [diag,icd[icd['icd_code'] == diag]['long_title'].to_string(index=False)]
+        
+        df_feats.loc[len(df_feats)]='Age'
+        df_feats.loc[len(df_feats)]='gender'
+        df_feats.loc[len(df_feats)]='ethnicity'
+        df_feats.loc[len(df_feats)]='insurance'
+        feats.extend(list(cond_df.columns))
+        feats.extend(list(demo.columns))
+
+        path = './data/dict/'+self.config.name.replace(" ","_")+'/features_description_'+self.encoding+'.csv'
+        df_feats.to_csv(path)
+        feat_tocsv=False
+        return feat_tocsv, feats
 ###########################################################RAW##################################################################
 
     def _info_raw(self):
@@ -448,21 +479,10 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             label = data['label']
             demo=demo.drop(['label'],axis=1)
             if feat_tocsv:
-                #save the features of the vector for analysis purposes if needed
-                if self.encoding == 'concat':
-                    feats = concat_cols.copy()
-                else:
-                    feats = list(dyn_df.columns.droplevel(0))
-                feats.extend(list(cond_df.columns))
-                feats.extend(list(demo.columns))
-                df_feats = pd.DataFrame(columns=feats)
-                path = './data/dict/'+self.config.name.replace(" ","_")+'/features_'+self.encoding+'.csv'
-                df_feats.to_csv(path)
-                feat_tocsv=False
+                feat_tocsv, feats  = self.save_features(concat_cols,dyn_df,cond_df,demo)
             X= generate_ml(dyn_df,cond_df,demo,concat_cols,self.concat)
             X=X.values[0]
             
-            
             size_concat = self.size_cond+ self.size_proc * self.interval + self.size_meds * self.interval+ self.size_out * self.interval+ self.size_chart *self.interval+ self.size_lab * self.interval + 4
             size_aggreg = self.size_cond+ self.size_proc + self.size_meds+ self.size_out+ self.size_chart+ self.size_lab + 4