task_cohort.py

import os
import sys
import yaml
import time
import yaml
import sys
from .day_intervals_cohort_v22 import extract_data
from .data_generation_icu_modify import Generator_icu
from .data_generation_modify import Generator

def check_config_file(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_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
    import feature_selection_icu
    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_file(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 = 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---------------------------------------------

    #----------------------------------------------FEATURE SELECTION---------------------------------------------
     
    if data_icu:
        if select_chart or select_out or select_diag or select_med or select_proc:
            if select_chart:
                input('Please edit list of codes in ./data/summary/chart_features.csv to select the chart items to keep and press enter to continue')
            if select_out:
                input('Please edit list of codes in ./data/summary/out_features.csv to select the output items to keep and press enter to continue')
            if select_diag:
                input('Please edit list of codes in ./data/summary/diag_features.csv to select the diagnosis ids to keep and press enter to continue')
            if select_med:
                input('Please edit list of codes in ./data/summary/med_features.csv to select the meds items to keep and press enter to continue')
            if select_proc:
                input('Please edit list of codes in ./data/summary/proc_features.csv to select the procedures ids to keep and press enter to continue')
        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:
        if select_diag or select_med or select_proc or select_lab:
            if select_diag:
                input('Please edit list of codes in ./data/summary/diag_features.csv to select the diagnosis ids to keep and press enter to continue')
            if select_med:
                input('Please edit list of codes in ./data/summary/med_features.csv to select the meds items to keep and press enter to continue')
            if select_proc:
                input('Please edit list of codes in ./data/summary/proc_features.csv to select the procedures ids to keep and press enter to continue')
            if select_lab:
                input('Please edit list of codes in ./data/summary/labs_features.csv to select the labs items to keep and press enter to continue')
        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=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=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============]")