File size: 5,799 Bytes
93f39a5 dde9150 93f39a5 dde9150 93f39a5 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 |
import ipywidgets as widgets
import sys
from pathlib import Path
import os
import importlib
import shutil
import time
import yaml
def task_cohort(task,mimic_path,path_benchmark):
root_dir = path_benchmark
sys.path.append(path_benchmark+'/preprocessing/day_intervals_preproc')
sys.path.append(path_benchmark+'/utils')
sys.path.append(path_benchmark+'/preprocessing/hosp_module_preproc')
sys.path.append(path_benchmark+'/model')
import day_intervals_cohort_v2
import day_intervals_cohort
import feature_selection_icu
import data_generation_icu_modify
version_path = mimic_path
version = version_path.split('/')[-1][0]
start = time.time()
#----------------------------------------------config----------------------------------------------------
if task=='Mortality':
with open('./config/mortality.config') as f:
config = yaml.safe_load(f)
elif task == 'Phenotype':
with open('./config/phenotype.config') as f:
config = yaml.safe_load(f)
elif task == 'Length of Stay':
with open('./config/los.config') as f:
config = yaml.safe_load(f)
elif task == 'Readmission':
with open('./config/readmission.config') as f:
config = yaml.safe_load(f)
disease_label = config['disease_label']
tim = config['time']
label = config['label']
timeW = config['timeW']
include=int(timeW.split()[1])
bucket = config['bucket']
radimp = config['radimp']
predW = config['predW']
diag_flag = config['diagnosis']
out_flag = config['output']
chart_flag = config['chart']
proc_flag= config['proc']
med_flag = config['meds']
disease_filter = config['disease_filter']
icu_no_icu = config['icu_no_icu']
groupingICD = config['groupingICD']
# -------------------------------------------------------------------------------------------------------------
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_v2.extract_data(icu_no_icu,label,tim,icd_code, root_dir,disease_label)
elif version == '1':
cohort_output = day_intervals_cohort.extract_data(icu_no_icu,label,tim,icd_code, root_dir,disease_label)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------FEATURES-------------------------------------------------------
if data_icu :
feature_selection_icu.feature_icu(cohort_output, version_path,diag_flag,out_flag,chart_flag,proc_flag,med_flag)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------GROUPING-------------------------------------------------------
group_diag=False
group_med=False
group_proc=False
if data_icu:
if diag_flag:
group_diag=groupingICD
feature_selection_icu.preprocess_features_icu(cohort_output, diag_flag, group_diag,False,False,False,0,0)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------SUMMARY-------------------------------------------------------
if data_icu:
feature_selection_icu.generate_summary_icu(cohort_output,diag_flag,proc_flag,med_flag,out_flag,chart_flag)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#----------------------------------------------FEATURE SELECTION---------------------------------------------
select_diag= config['select_diag']
select_med= config['select_med']
select_proc= config['select_proc']
#select_lab= config['select_lab']
select_out= config['select_out']
select_chart= config['select_chart']
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)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
#---------------------------------------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)
end = time.time()
print("Time elapsed : ", round((end - start)/60,2),"mins")
# ---------------------------------------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)
print("[============TASK COHORT SUCCESSFULLY CREATED============]")
|