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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 yaml
from git import Repo
import numpy as np
from .dataset_utils import vocab, concat_data, generate_deep, generate_ml, generate_text
from .task_cohort import create_cohort
################################################################################
################################################################################
## ##
## MIMIC IV DATASET GENERATION SCRIPT ##
## ##
################################################################################
################################################################################
_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.
Currently working with Mimic-IV ICU Data.
"""
_BASE_URL = "https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main"
_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
_GIT_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline"
_DATA_GEN = f"{_BASE_URL}/data_generation_icu_modify.py"
_DATA_GEN_HOSP= f"{_BASE_URL}/data_generation_modify.py"
_DAY_INT= f"{_BASE_URL}/day_intervals_cohort_v22.py"
_CONFIG_URLS = {'los' : f"{_BASE_URL}/config/los.config",
'mortality' : f"{_BASE_URL}/config/mortality.config",
'phenotype' : f"{_BASE_URL}/config/phenotype.config",
'readmission' : f"{_BASE_URL}/config/readmission.config"
}
class Mimic4DatasetConfig(datasets.BuilderConfig):
"""BuilderConfig for Mimic4Dataset."""
def __init__(
self,
**kwargs,
):
super().__init__(**kwargs)
class Mimic4Dataset(datasets.GeneratorBasedBuilder):
"""Create Mimic4Dataset dataset from Mimic-IV data stored in user machine."""
VERSION = datasets.Version("1.1.0")
def __init__(self, **kwargs):
self.mimic_path = kwargs.pop("mimic_path", None)
self.encoding = kwargs.pop("encoding",'concat')
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 init_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'):
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'
# path_bench = os.path.abspath('./').replace('\\','/')+'/MIMIC-IV-Data-Pipeline'
path_bench = './MIMIC-IV-Data-Pipeline'
if not os.path.exists(path_bench):
# subprocess.run(["git", "clone", repo_url, path_bench],shell = True,capture_output=True)
Repo.clone_from(repo_url, path_bench)
print("Repository cloned. \n")
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)
#get config parameters for time series and features
timeW = config['timeWindow']
self.timeW=int(timeW.split()[1])
self.bucket = config['timebucket']
self.predW = config['predW']
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.feat_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.feat_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')
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:
create_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
#verify if the dimension of the tensors corresponds to the time window
def verif_dim_tensor(self, proc, out, chart, meds, lab,interv):
verif=True
if self.feat_proc:
if (len(proc)!= interv):
verif=False
if self.feat_out:
if (len(out)!=interv):
verif=False
if self.feat_chart:
if (len(chart)!=interv):
verif=False
if self.feat_meds:
if (len(meds)!=interv):
verif=False
if self.feat_lab:
if (len(lab)!=interv):
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,index=False)
feat_tocsv=False
return feat_tocsv, feats
###########################################################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")),
"features_names" : datasets.Sequence(datasets.Value("string")),
}
)
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 = ['<PAD>', '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')
feat_tocsv=True
for i, data in df.iterrows():
dyn_df,cond_df,demo=concat_data(data,self.interval,self.feat_cond,self.feat_proc,self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict)
dyn=dyn_df.copy()
dyn.columns=dyn.columns.droplevel(0)
concat_cols = [f"{col}_{t}" for t in range(dyn.shape[0]) for col in dyn.columns]
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)
if feat_tocsv:
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
if ((self.concat and len(X)==size_concat) or ((not self.concat) and len(X)==size_aggreg)):
yield int(i), {
"label": label,
"features": X,
"features_names" : feats
}
######################################################DEEP###############################################################
def _info_deep(self):
features = datasets.Features(
{
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
"DEMO": datasets.Sequence(datasets.Value("int64")),
"COND" : datasets.Sequence(datasets.Value("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') ,
"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)
for key, data in dico.items():
stat, demo, meds, chart, out, proc, lab, y = generate_deep(data,self.interval, self.config.name.replace(" ","_"), self.feat_cond, self.feat_proc, self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab,self.condDict, self.procDict, self.outDict, self.chartDict, self.medDict, self.eth_vocab,self.gender_vocab,self.age_vocab,self.ins_vocab)
if self.verif_dim_tensor(proc, out, chart, meds, lab, self.interval):
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,
}
######################################################text##############################################################
def _info_text(self):
features = datasets.Features(
{
"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]),
"text" : datasets.Value(dtype='string', id=None),
}
)
return datasets.DatasetInfo(
description=_DESCRIPTION,
features=features,
homepage=_HOMEPAGE,
citation=_CITATION,
)
def _generate_examples_text(self, filepath):
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)
with open(filepath, 'rb') as fp:
dico = pickle.load(fp)
for key, data in dico.items():
cond_text,chart_text,meds_text,proc_text,out_text = generate_text(data,icd,items, self.feat_cond, self.feat_chart, self.feat_meds, self.feat_proc, self.feat_out)
text= cond_text+chart_text+meds_text+proc_text+out_text
yield int(key),{
'label' : data['label'],
'text': text
}
#############################################################################################################################
def _info(self):
self.path = self.init_cohort()
self.interval = (self.timeW//self.bucket)
self.size_cond, self.size_proc, self.size_meds, self.size_out, self.size_chart, self.size_lab, self.eth_vocab,self.gender_vocab,self.age_vocab,self.ins_vocab,self.condDict,self.procDict,self.medDict,self.outDict,self.chartDict,self.labDict=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' or self.encoding =='aggreg'):
return self._info_encoded()
elif self.encoding == 'tensor' :
return self._info_deep()
elif self.encoding == 'text' :
return self._info_text()
else:
return self._info_raw()
def _split_generators(self, dl_manager):
data_dir = "./data/dict/"+self.config.name.replace(" ","_")
if self.val_size > 0 :
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/train_data.pkl'}),
datasets.SplitGenerator(name=datasets.Split.VALIDATION, gen_kwargs={"filepath": data_dir+'/val_data.pkl'}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/test_data.pkl'}),
]
else :
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir+'/train_data.pkl'}),
datasets.SplitGenerator(name=datasets.Split.TEST, gen_kwargs={"filepath": data_dir+'/test_data.pkl'}),
]
def _generate_examples(self, filepath):
if (self.encoding == 'concat' or self.encoding == 'aggreg'):
yield from self._generate_examples_encoded(filepath)
elif self.encoding == 'tensor' :
yield from self._generate_examples_deep(filepath)
elif self.encoding == 'text' :
yield from self._generate_examples_text(filepath)
else :
yield from self._generate_examples_raw(filepath) |