Update Mimic4Dataset.py

Mimic4Dataset.py

@@ -27,6 +27,7 @@ _HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
 _CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
 _URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"
 _DATA_GEN = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_icu_modify.py'
+_DATA_GEN_HOSP= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/data_generation_modify.py'
 _DAY_INT= 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/day_intervals_cohort_v22.py'
 _CONFIG_URLS = {'los' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/los.config',
                 'mortality' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/mortality.config',
@@ -53,26 +54,42 @@ def check_config(task,config_file):
     predW = config['predW']
     disease_filter = config['disease_filter']
     icu_no_icu = config['icu_no_icu']
-    groupingICD = config['groupingICD']
+    groupingDiag = config['groupingDiag']
+
+    assert( icu_no_icu in ['ICU','Non-ICU' ], "Chossen data should be one of the following: ICU, Non-ICU")
+    data_icu = icu_no_icu=='ICU'
+    
+    if data_icu:
+        chart_flag = config['chart']
+        output_flag = config['output']
+        select_chart = config['select_chart']
+    else:
+        lab_flag =config['lab']
+        select_lab = config['select_lab']
+        groupingMed = config['groupingMed']
+        groupingProc = config['groupingProc']
+
 
-    chart_flag = config['chart']
-    output_flag = config['output']
     diag_flag= config['diagnosis']
     proc_flag = config['proc']
     meds_flag = config['meds']
-
     select_diag= config['select_diag']
     select_med= config['select_med']
     select_proc= config['select_proc']
     select_out = config['select_out']
-    select_chart = config['select_chart']
 
     outlier_removal=config['outlier_removal']
     thresh=config['outlier']
     left_thresh=config['left_outlier']
     
-    assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_chart,bool), " select_diag, select_chart, select_med, select_proc, select_out should be boolean")
-    assert (isinstance(chart_flag,bool) and isinstance(output_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "chart_flag, output_flag, diag_flag, proc_flag, meds_flag should be boolean")
+    if data_icu:
+        assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_chart,bool), " select_diag, select_chart, select_med, select_proc, select_out should be boolean")
+        assert (isinstance(chart_flag,bool) and isinstance(output_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "chart_flag, output_flag, diag_flag, proc_flag, meds_flag should be boolean")
+    
+    else:
+        assert (isinstance(select_diag,bool) and isinstance(select_med,bool) and isinstance(select_proc,bool) and isinstance(select_out,bool) and isinstance(select_lab,bool), " select_diag, select_lab, select_med, select_proc, select_out should be boolean")
+        assert (isinstance(lab_flag,bool) and isinstance(diag_flag,bool) and isinstance(proc_flag,bool) and isinstance(meds_flag,bool), "lab_flag, diag_flag, proc_flag, meds_flag should be boolean")
+    
     if task=='Phenotype':
         if disease_label=='Heart Failure':
             label='Readmission'
@@ -117,14 +134,19 @@ def check_config(task,config_file):
         raise ValueError('Task not correct')
     
     assert( disease_filter in ['Heart Failure','COPD','CKD','CAD',""], "Disease filter should be one of the following: Heart Failure, COPD, CKD, CAD or empty")
-    assert( icu_no_icu in ['ICU'], "Dataset currently only supports ICU data")
-    assert( groupingICD in ['Convert ICD-9 to ICD-10 and group ICD-10 codes','Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes'], "Grouping ICD should be one of the following: Convert ICD-9 to ICD-10 and group ICD-10 codes, Keep both ICD-9 and ICD-10 codes, Convert ICD-9 to ICD-10 codes")
+    assert( groupingDiag in ['Convert ICD-9 to ICD-10 and group ICD-10 codes','Keep both ICD-9 and ICD-10 codes','Convert ICD-9 to ICD-10 codes'], "Grouping ICD should be one of the following: Convert ICD-9 to ICD-10 and group ICD-10 codes, Keep both ICD-9 and ICD-10 codes, Convert ICD-9 to ICD-10 codes")
     assert (bucket<=6 and bucket>=1 and isinstance(bucket, int), "Time bucket should be between 1 and 6 and an integer")
     assert (radimp in ['No Imputation', 'forward fill and mean','forward fill and median'], "imputation should be one of the following: No Imputation, forward fill and mean, forward fill and median")
     if chart_flag:
         assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer")
         assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer")
         assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)")
+    if lab_flag:
+        assert (left_thresh>=0 and left_thresh<=10 and isinstance(left_thresh, int), "Left outlier threshold should be between 0 and 10 and an integer")
+        assert (thresh>=90 and thresh<=99 and isinstance(thresh, int), "Outlier threshold should be between 90 and 99 and an integer")
+        assert (outlier_removal in ['No outlier detection','Impute Outlier (default:98)','Remove outliers (default:98)'], "Outlier removal should be one of the following: No outlier detection, Impute Outlier (default:98), Remove outliers (default:98)")
+        assert (groupingProc in ['ICD-9 and ICD-10','ICD-10'], "Grouping procedure should be one of the following: ICD-9 and ICD-10, ICD-10")
+        assert (groupingMed in ['Yes','No'], "Do you want to group Medication codes to use Non propietary names? : Grouping medication should be one of the following: Yes, No")
 
     return label, time, disease_label, predW
 
@@ -201,7 +223,7 @@ def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag):
         
         return len(condVocabDict),len(procVocabDict),len(medVocabDict),len(outVocabDict),len(chartVocabDict),len(labVocabDict),ethVocabDict,genderVocabDict,ageVocabDict,insVocabDict
 
-def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
+def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
     meds=data['Med']
     proc = data['Proc']
     out = data['Out']
@@ -308,6 +330,30 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
             features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART'])
             features.columns=pd.MultiIndex.from_product([["CHART"], features.columns])
             chart_df=features.fillna(0)
+        
+        ##########LAB#########
+    if (feat_lab):
+        with open("./data/dict/"+task+"/labsVocab", 'rb') as fp:
+            chartDic = pickle.load(fp)
+
+        if chart:
+            charts=chart['val']
+            feat=charts.keys()
+            chart_val=[charts[key] for key in feat]
+            charts=pd.DataFrame(chartDic,columns=['LAB'])
+            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
+            features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
+            
+            chart=pd.DataFrame(columns=feat)
+            for c,v in zip(feat,chart_val):
+                chart[c]=v
+            chart.columns=pd.MultiIndex.from_product([["LAB"], chart.columns])
+            chart_df = pd.concat([features,chart],ignore_index=True).fillna(0)
+        else:
+            charts=pd.DataFrame(chartDic,columns=['LAB'])
+            features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['LAB'])
+            features.columns=pd.MultiIndex.from_product([["LAB"], features.columns])
+            chart_df=features.fillna(0)
     
     ###MEDS
     if (feat_meds):
@@ -334,7 +380,9 @@ def concat_data(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
 
     dyn_df = pd.concat([meds_df,proc_df,out_df,chart_df], axis=1)
     return dyn_df,cond_df,demo
-def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
+
+
+def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab):
     stat_df = torch.zeros(size=(1,0))
     demo_df = torch.zeros(size=(1,0))
     meds = torch.zeros(size=(0,0))
@@ -344,10 +392,9 @@ def getXY_deep(data,task,feat_cond,feat_proc,feat_out,feat_chart,feat_meds):
     lab = torch.zeros(size=(0,0))
     stat_df = torch.zeros(size=(1,0))
     demo_df = torch.zeros(size=(1,0))
-    feat_lab = False
     
     size_cond, size_proc, size_meds, size_out, size_chart, size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,False)
-    dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds)
+    dyn,cond_df,demo=concat_data(data,task.replace(" ","_"),feat_cond,feat_proc,feat_out,feat_chart,feat_meds,feat_lab)
 
     ###########""
     if feat_chart:
@@ -462,6 +509,9 @@ def getXY(dyn,stat,demo,concat_cols,concat):
 
 
 
+
+
+
 def task_cohort(task, mimic_path, config_path):
     sys.path.append('./preprocessing/day_intervals_preproc')
     sys.path.append('./utils')
@@ -471,6 +521,9 @@ def task_cohort(task, mimic_path, config_path):
     import day_intervals_cohort
     import feature_selection_icu
     import data_generation_icu_modify
+    import data_generation_modify
+    import feature_selection_hosp
+
     
     root_dir = os.path.dirname(os.path.abspath('UserInterface.ipynb'))
     config_path='./config/'+config_path
@@ -492,15 +545,18 @@ def task_cohort(task, mimic_path, config_path):
     chart_flag = config['chart']
     proc_flag= config['proc']
     med_flag = config['meds']
+    lab_flag = config['lab']
 
     disease_filter = config['disease_filter']
     icu_no_icu = config['icu_no_icu']
-    groupingICD = config['groupingICD']
+    groupingDiag = config['groupingDiag']
+    groupingMed = config['groupingMed']
+    groupingProc = config['groupingProc']
 
     select_diag= config['select_diag']
     select_med= config['select_med']
     select_proc=  config['select_proc']
-    #select_lab= config['select_lab']
+    select_lab= config['select_lab']
     select_out= config['select_out']
     select_chart=  config['select_chart']
 
@@ -532,17 +588,34 @@ def task_cohort(task, mimic_path, config_path):
     print(data_icu)
     if data_icu :
         feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
+    else:
+        feature_selection_hosp.feature_nonicu(cohort_output, version_path,diag_flag,lab_flag,proc_flag,med_flag)
     #----------------------------------------------GROUPING-------------------------------------------------------
     if data_icu:
         if diag_flag:
-            group_diag=groupingICD
+            group_diag=groupingDiag
         feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
+    
+    else:
+        if diag_flag:
+            group_diag=groupingDiag
+        if med_flag:
+            group_med=groupingMed
+        if proc_flag:
+            group_proc=groupingProc
+        feature_selection_hosp.preprocess_features_hosp(cohort_output, diag_flag,proc_flag,med_flag,False,group_diag,group_med,group_proc,False,False,0,0)
     #----------------------------------------------SUMMARY-------------------------------------------------------
     if data_icu:
         feature_selection_icu.generate_summary_icu(diag_flag,proc_flag,med_flag,out_flag,chart_flag)
+    else:
+        feature_selection_hosp.generate_summary_hosp(diag_flag,proc_flag,med_flag,lab_flag)
     #----------------------------------------------FEATURE SELECTION---------------------------------------------
 
-    feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
+    if data_icu:
+        feature_selection_icu.features_selection_icu(cohort_output, diag_flag,proc_flag,med_flag,out_flag, chart_flag,select_diag,select_med,select_proc,select_out,select_chart)
+    else:
+        feature_selection_hosp.features_selection_hosp(cohort_output, diag_flag,proc_flag,med_flag,lab_flag,select_diag,select_med,select_proc,select_lab)
+
     #---------------------------------------CLEANING OF FEATURES-----------------------------------------------
     thresh=0
     if data_icu:
@@ -553,6 +626,14 @@ def task_cohort(task, mimic_path, config_path):
             thresh=config['outlier']
             left_thresh=config['left_outlier']
         feature_selection_icu.preprocess_features_icu(cohort_output, False, False,chart_flag,clean_chart,impute_outlier_chart,thresh,left_thresh)
+    else:
+        if lab_flag:
+            outlier_removal=config['outlier_removal']
+            clean_chart=outlier_removal!='No outlier detection'
+            impute_outlier_chart=outlier_removal=='Impute Outlier (default:98)'
+            thresh=config['outlier']
+            left_thresh=config['left_outlier']
+        feature_selection_hosp.preprocess_features_hosp(cohort_output, False,False, False,lab_flag,False,False,False,clean_chart,impute_outlier_chart,thresh,left_thresh)
     # ---------------------------------------tim-Series Representation--------------------------------------------
     if radimp == 'forward fill and mean' :
         impute='Mean'
@@ -563,11 +644,16 @@ def task_cohort(task, mimic_path, config_path):
     
     if data_icu:
         gen=data_generation_icu_modify.Generator(task,cohort_output,data_mort,data_admn,data_los,diag_flag,proc_flag,out_flag,chart_flag,med_flag,impute,include,bucket,predW)
+    else:
+        gen=data_generation_modify.Generator(cohort_output,data_mort,data_admn,data_los,diag_flag,lab_flag,proc_flag,med_flag,impute,include,bucket,predW)
+    
     end = time.time()
     print("Time elapsed : ", round((end - start)/60,2),"mins")
     print("[============TASK COHORT SUCCESSFULLY CREATED============]")
 
 
+
+
  #############################################DATASET####################################################################   
 class Mimic4DatasetConfig(datasets.BuilderConfig):
     """BuilderConfig for Mimic4Dataset."""
@@ -676,13 +762,22 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             shutil.move(file_path,'./config')
         with open(self.conf) as f:
             config = yaml.safe_load(f)
-        self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out = config['diagnosis'], config['chart'], config['proc'],  config['meds'], config['output']
+
+        self.data_icu = config['icu_no_icu']=='ICU'
+        if self.data_icu:
+            self.feat_cond, self.feat_chart, self.feat_proc, self.feat_meds, self.feat_out, self.lab = config['diagnosis'], config['chart'], config['proc'],  config['meds'], config['output'], False
+        else:
+            self.feat_cond, self.feat_lab, self.feat_proc, self.feat_meds, self.feat_chart, self.out = config['diagnosis'], config['lab'], config['proc'],  config['meds'], False, False
 
         #####################downloads modules from hub
         if not os.path.exists('./model/data_generation_icu_modify.py'):
             file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py")
             shutil.move(file_path, './model')
 
+        if not os.path.exists('./model/data_generation_modify.py'):
+            file_path, head = urlretrieve(_DATA_GEN_HOSP, "data_generation_modify.py")
+            shutil.move(file_path, './model')
+
         if not os.path.exists('./preprocessing/day_intervals_preproc/day_intervals_cohort_v22.py'):
             file_path, head = urlretrieve(_DAY_INT, "day_intervals_cohort_v22.py")
             shutil.move(file_path, './preprocessing/day_intervals_preproc')
@@ -757,7 +852,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                             "id": datasets.Sequence(datasets.Value("int32")),
                             "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
                                 },
-                "CHART":
+                "CHART/LAB":
                     {
                         "signal" : {
                             "id": datasets.Sequence(datasets.Value("int32")),
@@ -787,7 +882,6 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             dataDic = pickle.load(fp)
         for hid, data in dataDic.items():
             proc_features = data['Proc']
-            chart_features = data['Chart']
             meds_features = data['Med']
             out_features = data['Out']
             cond_features = data['Cond']['fids']
@@ -807,6 +901,11 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             outs = {"id" : items_outs,
                   "value": values_outs}
 
+            if self.data_icu:
+                chart_features = data['Chart']
+            else:
+                chart_features = data['Lab']
+
             #chart signal
             if ('signal' in chart_features):
                 items_chart_sig = list(chart_features['signal'].keys())
@@ -861,6 +960,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                     "rate" : meds_rate,
                     "amount" : meds_amount}
             
+
             yield int(hid), {
                 "label" : label,
                 "gender" : gender,
@@ -869,12 +969,13 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                 "age" : age,
                 "COND" : cond_features,
                 "PROC" : procs,
-                "CHART" : charts,
+                "CHART/LAB" : charts,
                 "OUT" : outs,
                 "MEDS" : meds
             }
 
 
+
 ###########################################################ENCODED##################################################################
         
     def _info_encoded(self):
@@ -915,7 +1016,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
         
         for i, data in df.iterrows():
             concat_cols=[]
-            dyn_df,cond_df,demo=concat_data(data,task,self.feat_cond,self.feat_proc,self.feat_out, self.feat_chart, self.feat_meds)
+            dyn_df,cond_df,demo=concat_data(data,task,self.feat_cond,self.feat_proc,self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab)
             dyn=dyn_df.copy()
             dyn.columns=dyn.columns.droplevel(0)
             cols=dyn.columns
@@ -943,7 +1044,7 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
                 "COND" : datasets.Array2D(shape=(None, 1025), dtype='int64') ,
                 "MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') ,
                 "PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') ,
-                "CHART" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') ,
+                "CHART/LAB" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') ,
                 "OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') ,
                 
             }
@@ -961,17 +1062,29 @@ class Mimic4Dataset(datasets.GeneratorBasedBuilder):
             dico = pickle.load(fp)
         task=self.config.name.replace(" ","_")
         for key, data in dico.items():   
-            stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, task, self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds)
-
-            yield int(key), {
-                'label': y,
-                'DEMO': demo,
-                'COND': stat,
-                'MEDS': meds,
-                'PROC': proc,
-                'CHART': chart,
-                'OUT': out,
-                }
+            stat, demo, meds, chart, out, proc, lab, y = getXY_deep(data, task, self.feat_cond,  self.feat_proc,  self.feat_out,  self.feat_chart, self.feat_meds,self.feat_lab)
+
+            if self.data_icu:
+                yield int(key), {
+                    'label': y,
+                    'DEMO': demo,
+                    'COND': stat,
+                    'MEDS': meds,
+                    'PROC': proc,
+                    'CHART/LAB': chart,
+                    'OUT': out,
+                    }
+            else:
+                yield int(key), {
+                    'label': y,
+                    'DEMO': demo,
+                    'COND': stat,
+                    'MEDS': meds,
+                    'PROC': proc,
+                    'CHART/LAB': lab,
+                    'OUT': out,
+                    }
+            
     
 #############################################################################################################################
     def _info(self):