Mimic4Dataset / Mimic4Dataset.py
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import csv
import json
import os
import pandas as pd
import datasets
import sys
import pickle
import subprocess
import shutil
from urllib.request import urlretrieve
_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 : "absolute/path/to/mimic4data/mimiciv/2.2"
"""
_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/blob/main/data_generation_icu_modify.py'
_COHORT = 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/blob/main/cohort.py'
_CONFIG_URLS = {'los' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/blob/main/config/los.config',
'mortality' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/blob/main/config/mortality.config',
'phenotype' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/blob/main/config/phenotype.config',
'readmission' : 'https://huggingface.co/datasets/thbndi/Mimic4Dataset/blob/main/config/readmission.config'
}
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)
if self.mimic_path is None:
raise ValueError("You must specify the path of the mimic4 data")
if not os.path.exists(self.mimic_path):
raise ValueError("The path of the mimic4 data does not exist")
self.config_path = kwargs.pop("config_path",None)
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 _info(self):
features = datasets.Features(
{
"label": datasets.ClassLabel(names=["0", "1"]),
"gender": datasets.Value("string"),
"ethnicity": 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":
{
"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 _split_generators(self, dl_manager: datasets.DownloadManager()):
if self.config.name == 'Phenotype' and self.config_path is None : self.config_path = _CONFIG_URLS['phenotype']
if self.config.name == 'Readmission' and self.config_path is None : self.config_path = _CONFIG_URLS['readmission']
if self.config.name == 'Length of Stay' and self.config_path is None : self.config_path = _CONFIG_URLS['los']
if self.config.name == 'Mortality' and self.config_path is None : self.config_path = _CONFIG_URLS['mortality']
repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline'
if os.path.exists(os.path.dirname(os.path.abspath('MIMIC-IV-Data-Pipeline-main'))):
path_bench = os.path.dirname(os.path.abspath('MIMIC-IV-Data-Pipeline-main'))+'/MIMIC-IV-Data-Pipeline-main'
else:
repodir = os.getcwd()
path_bench = repodir+'/MIMIC-IV-Data-Pipeline-main'
subprocess.run(["git", "clone", repo_url, path_bench])
if not os.path.exists(path_bench+'/config'):
os.makedirs(path_bench+'/config')
if self.config_path[0:4] == 'http':
file_path, head = urlretrieve(self.config_path)
else :
file_path = self.config_path
if not os.path.exists(path_bench+'/config/'+self.config_path.split('/')[-1]):
shutil.move(file_path, path_bench+'/config')
if not os.path.exists(path_bench+'/model/data_generation_icu_modify.py'):
file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py")
shutil.move(file_path, path_bench+'/model')
if not os.path.exists(path_bench+'cohort.py'):
file_path, head = urlretrieve(_COHORT, "cohort.py")
shutil.move(file_path, path_bench)
data_dir = path_bench + "/data/dataDic"
sys.path.append(path_bench)
import cohort
cohort.task_cohort(self.config.name,self.mimic_path, path_bench, self.config_path)
return [
datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir}),
]
def _generate_examples(self, filepath):
with open(filepath, 'rb') as fp:
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']
eth= data['ethnicity']
age = data['age']
gender = data['gender']
label = data['label']
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}
#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,
"age" : age,
"COND" : cond_features,
"PROC" : procs,
"CHART" : charts,
"OUT" : outs,
"MEDS" : meds
}