import os import pandas as pd import datasets import sys import pickle import subprocess import shutil from urllib.request import urlretrieve from sklearn.model_selection import train_test_split from sklearn.preprocessing import LabelEncoder import numpy as np from tqdm import tqdm import yaml import time import torch _DESCRIPTION = """\ Dataset for mimic4 data, by default for the Mortality task. Available tasks are: Mortality, Length of Stay, Readmission, Phenotype. The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main' mimic path should have this form : "path/to/mimic4data/from/username/mimiciv/2.2" If you choose a Custom task provide a configuration file for the Time series. """ _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', 'phenotype' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/phenotype.config', 'readmission' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main/config/readmission.config' } def check_config(task,config_file): with open(config_file) as f: config = yaml.safe_load(f) if task=='Phenotype': disease_label = config['disease_label'] else : disease_label = "" time = config['timePrediction'] label = task timeW = config['timeWindow'] include=int(timeW.split()[1]) bucket = config['timebucket'] radimp = config['radimp'] predW = config['predW'] disease_filter = config['disease_filter'] icu_no_icu = config['icu_no_icu'] 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'] lab_flag = False select_lab = False else: lab_flag =config['lab'] select_lab = config['select_lab'] groupingMed = config['groupingMed'] groupingProc = config['groupingProc'] chart_flag = False output_flag = False select_chart = False 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'] outlier_removal=config['outlier_removal'] thresh=config['outlier'] left_thresh=config['left_outlier'] 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' time=30 disease_label='I50' elif disease_label=='CAD': label='Readmission' time=30 disease_label='I25' elif disease_label=='CKD': label='Readmission' time=30 disease_label='N18' elif disease_label=='COPD': label='Readmission' time=30 disease_label='J44' else : raise ValueError('Disease label not correct provide one in the list: Heart Failure, CAD, CKD, COPD') predW=0 assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72") elif task=='Mortality': time=0 label= 'Mortality' assert (predW<=8 and predW>=2, "Prediction window should be between 2 and 8") assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between First 24 and First 72") elif task=='Length of Stay': label= 'Length of Stay' assert (timeW[0]=='Fisrt' and include<=72 and include>=24, "Time window should be between Fisrt 24 and Fisrt 72") assert (time<=10 and time>=1, "Length of stay should be between 1 and 10") predW=0 elif task=='Readmission': label= 'Readmission' assert (timeW[0]=='Last' and include<=72 and include>=24, "Time window should be between Last 24 and Last 72") assert (time<=150 and time>=10 and time%10==0, "Readmission window should be between 10 and 150 with a step of 10") predW=0 else: 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( 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 def create_vocab(file,task): with open ('./data/dict/'+task+'/'+file, 'rb') as fp: condVocab = pickle.load(fp) condVocabDict={} condVocabDict[0]=0 for val in range(len(condVocab)): condVocabDict[condVocab[val]]= val+1 return condVocabDict def gender_vocab(): genderVocabDict={} genderVocabDict['']=0 genderVocabDict['M']=1 genderVocabDict['F']=2 return genderVocabDict def vocab(task,diag_flag,proc_flag,out_flag,chart_flag,med_flag,lab_flag): condVocabDict={} procVocabDict={} medVocabDict={} outVocabDict={} chartVocabDict={} labVocabDict={} ethVocabDict={} ageVocabDict={} genderVocabDict={} insVocabDict={} ethVocabDict=create_vocab('ethVocab',task) with open('./data/dict/'+task+'/ethVocabDict', 'wb') as fp: pickle.dump(ethVocabDict, fp) ageVocabDict=create_vocab('ageVocab',task) with open('./data/dict/'+task+'/ageVocabDict', 'wb') as fp: pickle.dump(ageVocabDict, fp) genderVocabDict=gender_vocab() with open('./data/dict/'+task+'/genderVocabDict', 'wb') as fp: pickle.dump(genderVocabDict, fp) insVocabDict=create_vocab('insVocab',task) with open('./data/dict/'+task+'/insVocabDict', 'wb') as fp: pickle.dump(insVocabDict, fp) if diag_flag: file='condVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: condVocabDict = pickle.load(fp) if proc_flag: file='procVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: procVocabDict = pickle.load(fp) if med_flag: file='medVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: medVocabDict = pickle.load(fp) if out_flag: file='outVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: outVocabDict = pickle.load(fp) if chart_flag: file='chartVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: chartVocabDict = pickle.load(fp) if lab_flag: file='labsVocab' with open ('./data/dict/'+task+'/'+file, 'rb') as fp: labVocabDict = pickle.load(fp) 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,feat_lab): meds=data['Med'] proc = data['Proc'] out = data['Out'] chart = data['Chart'] cond= data['Cond']['fids'] cond_df=pd.DataFrame() proc_df=pd.DataFrame() out_df=pd.DataFrame() chart_df=pd.DataFrame() meds_df=pd.DataFrame() #demographic demo=pd.DataFrame(columns=['Age','gender','ethnicity','label','insurance']) new_row = {'Age': data['age'], 'gender': data['gender'], 'ethnicity': data['ethnicity'], 'label': data['label'], 'insurance': data['insurance']} demo = demo.append(new_row, ignore_index=True) ##########COND######### if (feat_cond): #get all conds with open("./data/dict/"+task+"/condVocab", 'rb') as fp: conDict = pickle.load(fp) conds=pd.DataFrame(conDict,columns=['COND']) features=pd.DataFrame(np.zeros([1,len(conds)]),columns=conds['COND']) #onehot encode if(cond ==[]): cond_df=pd.DataFrame(np.zeros([1,len(features)]),columns=features['COND']) cond_df=cond_df.fillna(0) else: cond_df=pd.DataFrame(cond,columns=['COND']) cond_df['val']=1 cond_df=(cond_df.drop_duplicates()).pivot(columns='COND',values='val').reset_index(drop=True) cond_df=cond_df.fillna(0) oneh = cond_df.sum().to_frame().T combined_df = pd.concat([features,oneh],ignore_index=True).fillna(0) combined_oneh=combined_df.sum().to_frame().T cond_df=combined_oneh ##########PROC######### if (feat_proc): with open("./data/dict/"+task+"/procVocab", 'rb') as fp: procDic = pickle.load(fp) if proc : feat=proc.keys() proc_val=[proc[key] for key in feat] procedures=pd.DataFrame(procDic,columns=['PROC']) features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC']) features.columns=pd.MultiIndex.from_product([["PROC"], features.columns]) procs=pd.DataFrame(columns=feat) for p,v in zip(feat,proc_val): procs[p]=v procs.columns=pd.MultiIndex.from_product([["PROC"], procs.columns]) proc_df = pd.concat([features,procs],ignore_index=True).fillna(0) else: procedures=pd.DataFrame(procDic,columns=['PROC']) features=pd.DataFrame(np.zeros([1,len(procedures)]),columns=procedures['PROC']) features.columns=pd.MultiIndex.from_product([["PROC"], features.columns]) proc_df=features.fillna(0) ##########OUT######### if (feat_out): with open("./data/dict/"+task+"/outVocab", 'rb') as fp: outDic = pickle.load(fp) if out : feat=out.keys() out_val=[out[key] for key in feat] outputs=pd.DataFrame(outDic,columns=['OUT']) features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT']) features.columns=pd.MultiIndex.from_product([["OUT"], features.columns]) outs=pd.DataFrame(columns=feat) for o,v in zip(feat,out_val): outs[o]=v outs.columns=pd.MultiIndex.from_product([["OUT"], outs.columns]) out_df = pd.concat([features,outs],ignore_index=True).fillna(0) else: outputs=pd.DataFrame(outDic,columns=['OUT']) features=pd.DataFrame(np.zeros([1,len(outputs)]),columns=outputs['OUT']) features.columns=pd.MultiIndex.from_product([["OUT"], features.columns]) out_df=features.fillna(0) ##########CHART######### if (feat_chart): with open("./data/dict/"+task+"/chartVocab", '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=['CHART']) features=pd.DataFrame(np.zeros([1,len(charts)]),columns=charts['CHART']) features.columns=pd.MultiIndex.from_product([["CHART"], features.columns]) chart=pd.DataFrame(columns=feat) for c,v in zip(feat,chart_val): chart[c]=v chart.columns=pd.MultiIndex.from_product([["CHART"], chart.columns]) chart_df = pd.concat([features,chart],ignore_index=True).fillna(0) else: charts=pd.DataFrame(chartDic,columns=['CHART']) 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): with open("./data/dict/"+task+"/medVocab", 'rb') as fp: medDic = pickle.load(fp) if meds: feat=meds['signal'].keys() med_val=[meds['amount'][key] for key in feat] meds=pd.DataFrame(medDic,columns=['MEDS']) features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS']) features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns]) med=pd.DataFrame(columns=feat) for m,v in zip(feat,med_val): med[m]=v med.columns=pd.MultiIndex.from_product([["MEDS"], med.columns]) meds_df = pd.concat([features,med],ignore_index=True).fillna(0) else: meds=pd.DataFrame(medDic,columns=['MEDS']) features=pd.DataFrame(np.zeros([1,len(meds)]),columns=meds['MEDS']) features.columns=pd.MultiIndex.from_product([["MEDS"], features.columns]) meds_df=features.fillna(0) 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,feat_lab): stat_df = torch.zeros(size=(1,0)) demo_df = torch.zeros(size=(1,0)) meds = torch.zeros(size=(0,0)) charts = torch.zeros(size=(0,0)) proc = torch.zeros(size=(0,0)) out = torch.zeros(size=(0,0)) lab = torch.zeros(size=(0,0)) stat_df = torch.zeros(size=(1,0)) demo_df = torch.zeros(size=(1,0)) 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,feat_lab) ###########"" if feat_chart: charts = dyn['CHART'] charts=charts.to_numpy() charts = torch.tensor(charts, dtype=torch.long) charts = charts.tolist() if feat_meds: meds = dyn['MEDS'] meds=meds.to_numpy() meds = torch.tensor(meds, dtype=torch.long) meds = meds.tolist() if feat_proc: proc = dyn['PROC'] proc=proc.to_numpy() proc = torch.tensor(proc, dtype=torch.long) proc = proc.tolist() if feat_out: out = dyn['OUT'] out=out.to_numpy() out = torch.tensor(out, dtype=torch.long) out=out.view(out.shape[1],out.shape[2]) out = out.tolist() if feat_lab: lab = dyn['LAB'] lab=lab.to_numpy() lab = torch.tensor(lab, dtype=torch.long) lab = lab.tolist() ####################"" stat=cond_df stat = stat.to_numpy() stat = torch.tensor(stat) if stat_df[0].nelement(): stat_df = torch.cat((stat_df,stat),0) else: stat_df = stat y = int(demo['label']) demo["gender"].replace(gender_vocab, inplace=True) demo["ethnicity"].replace(eth_vocab, inplace=True) demo["insurance"].replace(ins_vocab, inplace=True) demo["Age"].replace(age_vocab, inplace=True) demo=demo[["gender","ethnicity","insurance","Age"]] demo = demo.values demo = torch.tensor(demo) if demo_df[0].nelement(): demo_df = torch.cat((demo_df,demo),0) else: demo_df = demo stat_df = torch.tensor(stat_df) stat_df = stat_df.type(torch.LongTensor) stat_df = stat_df.squeeze() demo_df = torch.tensor(demo_df) demo_df = demo_df.type(torch.LongTensor) demo_df=demo_df.squeeze() y_df = torch.tensor(y) y_df = y_df.type(torch.LongTensor) return stat_df, demo_df, meds, charts, out, proc, lab, y_df def getXY(dyn,stat,demo,concat_cols,concat): X_df=pd.DataFrame() if concat: dyna=dyn.copy() dyna.columns=dyna.columns.droplevel(0) dyna=dyna.to_numpy() dyna=np.nan_to_num(dyna, copy=False) dyna=dyna.reshape(1,-1) dyn_df=pd.DataFrame(data=dyna,columns=concat_cols) else: dyn_df=pd.DataFrame() for key in dyn.columns.levels[0]: dyn_temp=dyn[key] if ((key=="CHART") or (key=="MEDS")): agg=dyn_temp.aggregate("mean") agg=agg.reset_index() else: agg=dyn_temp.aggregate("max") agg=agg.reset_index() if dyn_df.empty: dyn_df=agg else: dyn_df=pd.concat([dyn_df,agg],axis=0) dyn_df=dyn_df.T dyn_df.columns = dyn_df.iloc[0] dyn_df=dyn_df.iloc[1:,:] X_df=pd.concat([dyn_df,stat],axis=1) X_df=pd.concat([X_df,demo],axis=1) return X_df def task_cohort(task, mimic_path, config_path): sys.path.append('./preprocessing/day_intervals_preproc') sys.path.append('./utils') sys.path.append('./preprocessing/hosp_module_preproc') sys.path.append('./model') import day_intervals_cohort_v22 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 with open(config_path) as f: config = yaml.safe_load(f) version_path = mimic_path+'/' print(version_path) version = mimic_path.split('/')[-1][0] start = time.time() #----------------------------------------------config---------------------------------------------------- label, tim, disease_label, predW = check_config(task,config_path) icu_no_icu = config['icu_no_icu'] timeW = config['timeWindow'] include=int(timeW.split()[1]) bucket = config['timebucket'] radimp = config['radimp'] diag_flag = config['diagnosis'] proc_flag= config['proc'] med_flag = config['meds'] disease_filter = config['disease_filter'] groupingDiag = config['groupingDiag'] select_diag= config['select_diag'] select_med= config['select_med'] select_proc= config['select_proc'] if icu_no_icu=='ICU': out_flag = config['output'] chart_flag = config['chart'] select_out= config['select_out'] select_chart= config['select_chart'] lab_flag = False select_lab = False else: lab_flag = config['lab'] groupingMed = config['groupingMed'] groupingProc = config['groupingProc'] select_lab= config['select_lab'] out_flag = False chart_flag = False select_out= False select_chart= False # ------------------------------------------------------------------------------------------------------------- data_icu=icu_no_icu=="ICU" data_mort=label=="Mortality" data_admn=label=='Readmission' data_los=label=='Length of Stay' if (disease_filter=="Heart Failure"): icd_code='I50' elif (disease_filter=="CKD"): icd_code='N18' elif (disease_filter=="COPD"): icd_code='J44' elif (disease_filter=="CAD"): icd_code='I25' else: icd_code='No Disease Filter' #-----------------------------------------------EXTRACT MIMIC----------------------------------------------------- if version == '2': cohort_output = day_intervals_cohort_v22.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label) elif version == '1': cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,version_path,disease_label) #----------------------------------------------FEATURES------------------------------------------------------- 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=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--------------------------------------------- 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: if chart_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_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' elif radimp =='forward fill and median': impute = 'Median' else : impute = False 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.""" def __init__( self, **kwargs, ): super().__init__(**kwargs) class Mimic4Dataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") def __init__(self, **kwargs): self.mimic_path = kwargs.pop("mimic_path", None) self.encoding = kwargs.pop("encoding",'raw') self.config_path = kwargs.pop("config_path",None) self.test_size = kwargs.pop("test_size",0.2) self.val_size = kwargs.pop("val_size",0.1) self.generate_cohort = kwargs.pop("generate_cohort",True) if self.encoding == 'concat': self.concat = True else: self.concat = False super().__init__(**kwargs) BUILDER_CONFIGS = [ Mimic4DatasetConfig( name="Phenotype", version=VERSION, description="Dataset for mimic4 Phenotype task" ), Mimic4DatasetConfig( name="Readmission", version=VERSION, description="Dataset for mimic4 Readmission task" ), Mimic4DatasetConfig( name="Length of Stay", version=VERSION, description="Dataset for mimic4 Length of Stay task" ), Mimic4DatasetConfig( name="Mortality", version=VERSION, description="Dataset for mimic4 Mortality task" ), ] DEFAULT_CONFIG_NAME = "Mortality" def create_cohort(self): if self.config_path==None: if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype'] if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission'] if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los'] if self.config.name == 'Mortality' : self.config_path = _CONFIG_URLS['mortality'] version = self.mimic_path.split('/')[-1] mimic_folder= self.mimic_path.split('/')[-2] mimic_complete_path='/'+mimic_folder+'/'+version current_directory = os.getcwd() if os.path.exists(os.path.dirname(current_directory)+'/MIMIC-IV-Data-Pipeline-main'): dir =os.path.dirname(current_directory) os.chdir(dir) else: #move to parent directory of mimic data dir = self.mimic_path.replace(mimic_complete_path,'') if dir[-1]!='/': dir=dir+'/' elif dir=='': dir="./" parent_dir = os.path.dirname(self.mimic_path) os.chdir(parent_dir) #####################clone git repo if doesnt exists repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline' if os.path.exists('MIMIC-IV-Data-Pipeline-main'): path_bench = './MIMIC-IV-Data-Pipeline-main' else: path_bench ='./MIMIC-IV-Data-Pipeline-main' subprocess.run(["git", "clone", repo_url, path_bench]) os.makedirs(path_bench+'/mimic-iv') shutil.move(version,path_bench+'/mimic-iv') os.chdir(path_bench) self.mimic_path = './mimic-iv/'+version ####################Get configurations param #download config file if not custom if self.config_path[0:4] == 'http': c = self.config_path.split('/')[-1] file_path, head = urlretrieve(self.config_path,c) else : file_path = self.config_path if not os.path.exists('./config'): os.makedirs('config') #save config file in config folder self.conf='./config/'+file_path.split('/')[-1] if not os.path.exists(self.conf): shutil.move(file_path,'./config') with open(self.conf) as f: config = yaml.safe_load(f) timeW = config['timeWindow'] self.timeW=int(timeW.split()[1]) self.bucket = config['timebucket'] 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 self.feat_lab = 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 self.feat_out = False self.feat_chart = 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') data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic" sys.path.append(path_bench) config = self.config_path.split('/')[-1] #####################create task cohort if self.generate_cohort: task_cohort(self.config.name.replace(" ","_"),self.mimic_path,config) #####################Split data into train, test and val with open(data_dir, 'rb') as fp: dataDic = pickle.load(fp) data = pd.DataFrame.from_dict(dataDic) dict_dir = "./data/dict/"+self.config.name.replace(" ","_") data=data.T train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42) if self.val_size > 0 : train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42) val_dic = val_data.to_dict('index') val_path = dict_dir+'/val_data.pkl' with open(val_path, 'wb') as f: pickle.dump(val_dic, f) train_dic = train_data.to_dict('index') test_dic = test_data.to_dict('index') train_path = dict_dir+'/train_data.pkl' test_path = dict_dir+'/test_data.pkl' with open(train_path, 'wb') as f: pickle.dump(train_dic, f) with open(test_path, 'wb') as f: pickle.dump(test_dic, f) return dict_dir ###########################################################RAW################################################################## def _info_raw(self): features = datasets.Features( { "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), "gender": datasets.Value("string"), "ethnicity": datasets.Value("string"), "insurance": datasets.Value("string"), "age": datasets.Value("int32"), "COND": datasets.Sequence(datasets.Value("string")), "MEDS": { "signal": { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) } , "rate": { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) } , "amount": { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) } }, "PROC": { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) }, "CHART/LAB": { "signal" : { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) }, "val" : { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) }, }, "OUT": { "id": datasets.Sequence(datasets.Value("int32")), "value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) }, } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, ) def _generate_examples_raw(self, filepath): with open(filepath, 'rb') as fp: dataDic = pickle.load(fp) for hid, data in dataDic.items(): proc_features = data['Proc'] meds_features = data['Med'] out_features = data['Out'] cond_features = data['Cond']['fids'] eth= data['ethnicity'] age = data['age'] gender = data['gender'] label = data['label'] insurance=data['insurance'] items = list(proc_features.keys()) values =[proc_features[i] for i in items ] procs = {"id" : items, "value": values} items_outs = list(out_features.keys()) values_outs =[out_features[i] for i in items_outs ] 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()) values_chart_sig =[chart_features['signal'][i] for i in items_chart_sig ] chart_sig = {"id" : items_chart_sig, "value": values_chart_sig} else: chart_sig = {"id" : [], "value": []} #chart val if ('val' in chart_features): items_chart_val = list(chart_features['val'].keys()) values_chart_val =[chart_features['val'][i] for i in items_chart_val ] chart_val = {"id" : items_chart_val, "value": values_chart_val} else: chart_val = {"id" : [], "value": []} charts = {"signal" : chart_sig, "val" : chart_val} #meds signal if ('signal' in meds_features): items_meds_sig = list(meds_features['signal'].keys()) values_meds_sig =[meds_features['signal'][i] for i in items_meds_sig ] meds_sig = {"id" : items_meds_sig, "value": values_meds_sig} else: meds_sig = {"id" : [], "value": []} #meds rate if ('rate' in meds_features): items_meds_rate = list(meds_features['rate'].keys()) values_meds_rate =[meds_features['rate'][i] for i in items_meds_rate ] meds_rate = {"id" : items_meds_rate, "value": values_meds_rate} else: meds_rate = {"id" : [], "value": []} #meds amount if ('amount' in meds_features): items_meds_amount = list(meds_features['amount'].keys()) values_meds_amount =[meds_features['amount'][i] for i in items_meds_amount ] meds_amount = {"id" : items_meds_amount, "value": values_meds_amount} else: meds_amount = {"id" : [], "value": []} meds = {"signal" : meds_sig, "rate" : meds_rate, "amount" : meds_amount} yield int(hid), { "label" : label, "gender" : gender, "ethnicity" : eth, "insurance" : insurance, "age" : age, "COND" : cond_features, "PROC" : procs, "CHART/LAB" : charts, "OUT" : outs, "MEDS" : meds } ###########################################################ENCODED################################################################## def _info_encoded(self): features = datasets.Features( { "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), "features" : datasets.Sequence(datasets.Value("float32")), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, ) def _generate_examples_encoded(self, filepath): path= './data/dict/'+self.config.name.replace(" ","_")+'/ethVocab' with open(path, 'rb') as fp: ethVocab = pickle.load(fp) path= './data/dict/'+self.config.name.replace(" ","_")+'/insVocab' with open(path, 'rb') as fp: insVocab = pickle.load(fp) genVocab = ['', 'M', 'F'] gen_encoder = LabelEncoder() eth_encoder = LabelEncoder() ins_encoder = LabelEncoder() gen_encoder.fit(genVocab) eth_encoder.fit(ethVocab) ins_encoder.fit(insVocab) with open(filepath, 'rb') as fp: dico = pickle.load(fp) df = pd.DataFrame.from_dict(dico, orient='index') task=self.config.name.replace(" ","_") 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,self.feat_lab) dyn=dyn_df.copy() dyn.columns=dyn.columns.droplevel(0) cols=dyn.columns time=dyn.shape[0] for t in range(time): cols_t = [str(x) + "_"+str(t) for x in cols] concat_cols.extend(cols_t) demo['gender']=gen_encoder.transform(demo['gender']) demo['ethnicity']=eth_encoder.transform(demo['ethnicity']) demo['insurance']=ins_encoder.transform(demo['insurance']) label = data['label'] demo=demo.drop(['label'],axis=1) X= getXY(dyn_df,cond_df,demo,concat_cols,self.concat) X=X.values.tolist()[0] yield int(i), { "label": label, "features": X, } ######################################################DEEP############################################################### def _info_deep(self): features = datasets.Features( { "label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), #"DEMO": datasets.Array2D(shape=(None, 4), dtype='int64') , "DEMO": datasets.Sequence(datasets.Value("int64")), "COND" : datasets.Sequence(datasets.Value("int64")), #"COND" : datasets.Array2D(shape=(None, self.size_cond), dtype='int64') , "MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') , "PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') , "CHART/LAB" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') , #"CHART/LAB" : datasets.Sequence(datasets.Sequence(datasets.Value("int64"))), "OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') , } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, ) def _generate_examples_deep(self, filepath): with open(filepath, 'rb') as fp: 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,self.feat_lab) verri=True if self.feat_proc: if (len(proc)<(self.timeW//self.bucket)): verri=False if self.feat_out: if (len(out)<(self.timeW//self.bucket)): verri=False if self.feat_chart: if (len(chart)<(self.timeW//self.bucket)): verri=False if self.feat_meds: if (len(meds)<(self.timeW//self.bucket)): verri=False if self.feat_lab: if (len(lab)<(self.timeW//self.bucket)): verri=False if verri: 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, } else: continue ############################################################################################################################# def _info(self): self.path = self.create_cohort() self.size_cond, self.size_proc, self.size_meds, self.size_out, self.size_chart, self.size_lab, eth_vocab,gender_vocab,age_vocab,ins_vocab=vocab(self.config.name.replace(" ","_"),self.feat_cond,self.feat_proc,self.feat_out,self.feat_chart,self.feat_meds,self.feat_lab) if self.encoding == 'concat' : return self._info_encoded() elif self.encoding == 'aggreg' : return self._info_encoded() elif self.encoding == 'tensor' : return self._info_deep() else: return self._info_raw() def _split_generators(self, dl_manager): csv_dir = "./data/dict/"+self.config.name.replace(" ","_") if self.val_size > 0 : return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}), datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": csv_dir+'/val_data.pkl'}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}), ] else : return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": csv_dir+'/train_data.pkl'}), datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": csv_dir+'/test_data.pkl'}), ] def _generate_examples(self, filepath): if self.encoding == 'concat' : yield from self._generate_examples_encoded(filepath) elif self.encoding == 'aggreg' : yield from self._generate_examples_encoded(filepath) elif self.encoding == 'tensor' : yield from self._generate_examples_deep(filepath) else : yield from self._generate_examples_raw(filepath)