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import os |
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import pandas as pd |
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import datasets |
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import sys |
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import pickle |
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import subprocess |
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import shutil |
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from urllib.request import urlretrieve |
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from sklearn.model_selection import train_test_split |
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from sklearn.preprocessing import LabelEncoder |
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import yaml |
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from .dataset_utils import create_vocab,gender_vocab,vocab, concat_data, generate_deep, generate_ml |
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from .task_cohort import create_cohort, check_config_file |
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_DESCRIPTION = """\ |
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Dataset for mimic4 data, by default for the Mortality task. |
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Available tasks are: Mortality, Length of Stay, Readmission, Phenotype. |
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The data is extracted from the mimic4 database using this pipeline: 'https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main' |
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mimic path should have this form : "path/to/mimic4data/from/username/mimiciv/2.2" |
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If you choose a Custom task provide a configuration file for the Time series. |
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Currently working with Mimic-IV version 1 and 2 |
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""" |
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_BASE_URL = "https://huggingface.co/datasets/thbndi/Mimic4Dataset/resolve/main" |
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_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset" |
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_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html" |
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_GIT_URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline" |
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_DATA_GEN = f"{_BASE_URL}/data_generation_icu_modify.py" |
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_DATA_GEN_HOSP= f"{_BASE_URL}/data_generation_modify.py" |
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_DAY_INT= f"{_BASE_URL}/day_intervals_cohort_v22.py" |
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_CONFIG_URLS = {'los' : f"{_BASE_URL}/config/los.config", |
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'mortality' : f"{_BASE_URL}/config/mortality.config", |
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'phenotype' : f"{_BASE_URL}/config/phenotype.config", |
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'readmission' : f"{_BASE_URL}/config/readmission.config" |
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} |
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class Mimic4DatasetConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Mimic4Dataset.""" |
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def __init__( |
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self, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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class Mimic4Dataset(datasets.GeneratorBasedBuilder): |
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"""Create Mimic4Dataset dataset from Mimic-IV data stored in user machine.""" |
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VERSION = datasets.Version("1.0.0") |
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def __init__(self, **kwargs): |
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self.mimic_path = kwargs.pop("mimic_path", None) |
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self.encoding = kwargs.pop("encoding",'concat') |
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self.config_path = kwargs.pop("config_path",None) |
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self.test_size = kwargs.pop("test_size",0.2) |
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self.val_size = kwargs.pop("val_size",0.1) |
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self.generate_cohort = kwargs.pop("generate_cohort",True) |
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if self.encoding == 'concat': |
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self.concat = True |
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else: |
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self.concat = False |
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super().__init__(**kwargs) |
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BUILDER_CONFIGS = [ |
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Mimic4DatasetConfig( |
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name="Phenotype", |
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version=VERSION, |
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description="Dataset for mimic4 Phenotype task" |
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), |
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Mimic4DatasetConfig( |
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name="Readmission", |
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version=VERSION, |
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description="Dataset for mimic4 Readmission task" |
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), |
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Mimic4DatasetConfig( |
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name="Length of Stay", |
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version=VERSION, |
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description="Dataset for mimic4 Length of Stay task" |
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), |
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Mimic4DatasetConfig( |
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name="Mortality", |
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version=VERSION, |
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description="Dataset for mimic4 Mortality task" |
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), |
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] |
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DEFAULT_CONFIG_NAME = "Mortality" |
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def create_cohort(self): |
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if self.config_path==None: |
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if self.config.name == 'Phenotype' : self.config_path = _CONFIG_URLS['phenotype'] |
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if self.config.name == 'Readmission' : self.config_path = _CONFIG_URLS['readmission'] |
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if self.config.name == 'Length of Stay' : self.config_path = _CONFIG_URLS['los'] |
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if self.config.name == 'Mortality' : self.config_path = _CONFIG_URLS['mortality'] |
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version = self.mimic_path.split('/')[-1] |
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mimic_folder= self.mimic_path.split('/')[-2] |
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mimic_complete_path='/'+mimic_folder+'/'+version |
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current_directory = os.getcwd() |
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if os.path.exists(os.path.dirname(current_directory)+'/MIMIC-IV-Data-Pipeline-main'): |
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dir =os.path.dirname(current_directory) |
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os.chdir(dir) |
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else: |
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dir = self.mimic_path.replace(mimic_complete_path,'') |
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if dir[-1]!='/': |
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dir=dir+'/' |
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elif dir=='': |
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dir="./" |
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parent_dir = os.path.dirname(self.mimic_path) |
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os.chdir(parent_dir) |
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repo_url='https://github.com/healthylaife/MIMIC-IV-Data-Pipeline' |
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if os.path.exists('MIMIC-IV-Data-Pipeline-main'): |
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path_bench = './MIMIC-IV-Data-Pipeline-main' |
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else: |
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path_bench ='./MIMIC-IV-Data-Pipeline-main' |
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subprocess.run(["git", "clone", repo_url, path_bench]) |
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os.makedirs(path_bench+'/mimic-iv') |
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shutil.move(version,path_bench+'/mimic-iv') |
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os.chdir(path_bench) |
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self.mimic_path = './mimic-iv/'+version |
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if self.config_path[0:4] == 'http': |
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c = self.config_path.split('/')[-1] |
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file_path, head = urlretrieve(self.config_path,c) |
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else : |
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file_path = self.config_path |
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if not os.path.exists('./config'): |
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os.makedirs('config') |
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self.conf='./config/'+file_path.split('/')[-1] |
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if not os.path.exists(self.conf): |
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shutil.move(file_path,'./config') |
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with open(self.conf) as f: |
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config = yaml.safe_load(f) |
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timeW = config['timeWindow'] |
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self.timeW=int(timeW.split()[1]) |
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self.bucket = config['timebucket'] |
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self.data_icu = config['icu_no_icu']=='ICU' |
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if self.data_icu: |
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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 |
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self.feat_lab = False |
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else: |
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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 |
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self.feat_out = False |
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self.feat_chart = False |
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if not os.path.exists('./model/data_generation_icu_modify.py'): |
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file_path, head = urlretrieve(_DATA_GEN, "data_generation_icu_modify.py") |
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shutil.move(file_path, './model') |
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if not os.path.exists('./model/data_generation_modify.py'): |
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file_path, head = urlretrieve(_DATA_GEN_HOSP, "data_generation_modify.py") |
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shutil.move(file_path, './model') |
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if not os.path.exists('./preprocessing/day_intervals_preproc/day_intervals_cohort_v22.py'): |
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file_path, head = urlretrieve(_DAY_INT, "day_intervals_cohort_v22.py") |
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shutil.move(file_path, './preprocessing/day_intervals_preproc') |
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data_dir = "./data/dict/"+self.config.name.replace(" ","_")+"/dataDic" |
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sys.path.append(path_bench) |
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config = self.config_path.split('/')[-1] |
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if self.generate_cohort: |
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create_cohort(self.config.name.replace(" ","_"),self.mimic_path,config) |
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with open(data_dir, 'rb') as fp: |
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dataDic = pickle.load(fp) |
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data = pd.DataFrame.from_dict(dataDic) |
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dict_dir = "./data/dict/"+self.config.name.replace(" ","_") |
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data=data.T |
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train_data, test_data = train_test_split(data, test_size=self.test_size, random_state=42) |
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if self.val_size > 0 : |
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train_data, val_data = train_test_split(train_data, test_size=self.val_size, random_state=42) |
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val_dic = val_data.to_dict('index') |
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val_path = dict_dir+'/val_data.pkl' |
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with open(val_path, 'wb') as f: |
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pickle.dump(val_dic, f) |
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train_dic = train_data.to_dict('index') |
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test_dic = test_data.to_dict('index') |
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train_path = dict_dir+'/train_data.pkl' |
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test_path = dict_dir+'/test_data.pkl' |
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with open(train_path, 'wb') as f: |
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pickle.dump(train_dic, f) |
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with open(test_path, 'wb') as f: |
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pickle.dump(test_dic, f) |
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return dict_dir |
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def _info_raw(self): |
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features = datasets.Features( |
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{ |
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"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
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"gender": datasets.Value("string"), |
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"ethnicity": datasets.Value("string"), |
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"insurance": datasets.Value("string"), |
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"age": datasets.Value("int32"), |
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"COND": datasets.Sequence(datasets.Value("string")), |
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"MEDS": { |
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"signal": |
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{ |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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} |
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, |
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"rate": |
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{ |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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} |
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, |
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"amount": |
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{ |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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} |
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}, |
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"PROC": { |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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}, |
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"CHART/LAB": |
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{ |
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"signal" : { |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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}, |
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"val" : { |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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}, |
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}, |
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"OUT": { |
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"id": datasets.Sequence(datasets.Value("int32")), |
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"value": datasets.Sequence(datasets.Sequence(datasets.Value("float32"))) |
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}, |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _generate_examples_raw(self, filepath): |
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with open(filepath, 'rb') as fp: |
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dataDic = pickle.load(fp) |
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for hid, data in dataDic.items(): |
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proc_features = data['Proc'] |
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meds_features = data['Med'] |
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out_features = data['Out'] |
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cond_features = data['Cond']['fids'] |
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eth= data['ethnicity'] |
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age = data['age'] |
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gender = data['gender'] |
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label = data['label'] |
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insurance=data['insurance'] |
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items = list(proc_features.keys()) |
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values =[proc_features[i] for i in items ] |
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procs = {"id" : items, |
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"value": values} |
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items_outs = list(out_features.keys()) |
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values_outs =[out_features[i] for i in items_outs ] |
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outs = {"id" : items_outs, |
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"value": values_outs} |
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if self.data_icu: |
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chart_features = data['Chart'] |
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else: |
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chart_features = data['Lab'] |
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if ('signal' in chart_features): |
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items_chart_sig = list(chart_features['signal'].keys()) |
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values_chart_sig =[chart_features['signal'][i] for i in items_chart_sig ] |
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chart_sig = {"id" : items_chart_sig, |
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"value": values_chart_sig} |
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else: |
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chart_sig = {"id" : [], |
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"value": []} |
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if ('val' in chart_features): |
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items_chart_val = list(chart_features['val'].keys()) |
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values_chart_val =[chart_features['val'][i] for i in items_chart_val ] |
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chart_val = {"id" : items_chart_val, |
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"value": values_chart_val} |
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else: |
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chart_val = {"id" : [], |
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"value": []} |
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charts = {"signal" : chart_sig, |
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"val" : chart_val} |
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if ('signal' in meds_features): |
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items_meds_sig = list(meds_features['signal'].keys()) |
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values_meds_sig =[meds_features['signal'][i] for i in items_meds_sig ] |
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meds_sig = {"id" : items_meds_sig, |
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"value": values_meds_sig} |
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else: |
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meds_sig = {"id" : [], |
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"value": []} |
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if ('rate' in meds_features): |
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items_meds_rate = list(meds_features['rate'].keys()) |
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values_meds_rate =[meds_features['rate'][i] for i in items_meds_rate ] |
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meds_rate = {"id" : items_meds_rate, |
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"value": values_meds_rate} |
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else: |
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meds_rate = {"id" : [], |
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"value": []} |
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if ('amount' in meds_features): |
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items_meds_amount = list(meds_features['amount'].keys()) |
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values_meds_amount =[meds_features['amount'][i] for i in items_meds_amount ] |
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meds_amount = {"id" : items_meds_amount, |
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"value": values_meds_amount} |
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else: |
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meds_amount = {"id" : [], |
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"value": []} |
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meds = {"signal" : meds_sig, |
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"rate" : meds_rate, |
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"amount" : meds_amount} |
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yield int(hid), { |
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"label" : label, |
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"gender" : gender, |
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"ethnicity" : eth, |
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"insurance" : insurance, |
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"age" : age, |
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"COND" : cond_features, |
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"PROC" : procs, |
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"CHART/LAB" : charts, |
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"OUT" : outs, |
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"MEDS" : meds |
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} |
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def _info_encoded(self): |
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features = datasets.Features( |
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{ |
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"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
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"features" : datasets.Sequence(datasets.Value("float32")), |
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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def _generate_examples_encoded(self, filepath): |
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path= './data/dict/'+self.config.name.replace(" ","_")+'/ethVocab' |
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with open(path, 'rb') as fp: |
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ethVocab = pickle.load(fp) |
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path= './data/dict/'+self.config.name.replace(" ","_")+'/insVocab' |
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with open(path, 'rb') as fp: |
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insVocab = pickle.load(fp) |
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genVocab = ['<PAD>', 'M', 'F'] |
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gen_encoder = LabelEncoder() |
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eth_encoder = LabelEncoder() |
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ins_encoder = LabelEncoder() |
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gen_encoder.fit(genVocab) |
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eth_encoder.fit(ethVocab) |
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ins_encoder.fit(insVocab) |
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with open(filepath, 'rb') as fp: |
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dico = pickle.load(fp) |
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df = pd.DataFrame.from_dict(dico, orient='index') |
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task=self.config.name.replace(" ","_") |
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for i, data in df.iterrows(): |
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concat_cols=[] |
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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) |
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dyn=dyn_df.copy() |
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dyn.columns=dyn.columns.droplevel(0) |
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cols=dyn.columns |
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time=dyn.shape[0] |
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for t in range(time): |
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cols_t = [str(x) + "_"+str(t) for x in cols] |
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concat_cols.extend(cols_t) |
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demo['gender']=gen_encoder.transform(demo['gender']) |
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demo['ethnicity']=eth_encoder.transform(demo['ethnicity']) |
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demo['insurance']=ins_encoder.transform(demo['insurance']) |
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label = data['label'] |
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demo=demo.drop(['label'],axis=1) |
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X= generate_ml(dyn_df,cond_df,demo,concat_cols,self.concat) |
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X=X.values.tolist()[0] |
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yield int(i), { |
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"label": label, |
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"features": X, |
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} |
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|
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def _info_deep(self): |
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features = datasets.Features( |
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{ |
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"label": datasets.ClassLabel(num_classes=2,names=["0", "1"]), |
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"DEMO": datasets.Sequence(datasets.Value("int64")), |
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"COND" : datasets.Sequence(datasets.Value("int64")), |
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"MEDS" : datasets.Array2D(shape=(None, self.size_meds), dtype='int64') , |
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"PROC" : datasets.Array2D(shape=(None, self.size_proc), dtype='int64') , |
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"CHART/LAB" : datasets.Array2D(shape=(None, self.size_chart), dtype='int64') , |
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"OUT" : datasets.Array2D(shape=(None, self.size_out), dtype='int64') , |
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|
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} |
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) |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=features, |
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homepage=_HOMEPAGE, |
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citation=_CITATION, |
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) |
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|
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|
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def _generate_examples_deep(self, filepath): |
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with open(filepath, 'rb') as fp: |
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dico = pickle.load(fp) |
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task=self.config.name.replace(" ","_") |
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for key, data in dico.items(): |
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stat, demo, meds, chart, out, proc, lab, y = generate_deep(data, task, self.feat_cond, self.feat_proc, self.feat_out, self.feat_chart, self.feat_meds,self.feat_lab) |
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|
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verri=True |
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if self.feat_proc: |
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if (len(proc)<(self.timeW//self.bucket)): |
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verri=False |
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if self.feat_out: |
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if (len(out)<(self.timeW//self.bucket)): |
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verri=False |
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if self.feat_chart: |
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if (len(chart)<(self.timeW//self.bucket)): |
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verri=False |
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if self.feat_meds: |
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if (len(meds)<(self.timeW//self.bucket)): |
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verri=False |
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if self.feat_lab: |
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if (len(lab)<(self.timeW//self.bucket)): |
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verri=False |
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if verri: |
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if self.data_icu: |
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yield int(key), { |
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'label': y, |
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'DEMO': demo, |
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'COND': stat, |
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'MEDS': meds, |
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'PROC': proc, |
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'CHART/LAB': chart, |
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'OUT': out, |
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} |
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else: |
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yield int(key), { |
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'label': y, |
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'DEMO': demo, |
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'COND': stat, |
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'MEDS': meds, |
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'PROC': proc, |
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'CHART/LAB': lab, |
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'OUT': out, |
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} |
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else: |
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continue |
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|
|
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|
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def _info(self): |
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self.path = self.create_cohort() |
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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) |
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|
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if self.encoding == 'concat' : |
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return self._info_encoded() |
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|
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elif self.encoding == 'aggreg' : |
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return self._info_encoded() |
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|
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elif self.encoding == 'tensor' : |
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return self._info_deep() |
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|
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else: |
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return self._info_raw() |
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|
|
|
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def _split_generators(self, dl_manager): |
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csv_dir = "./data/dict/"+self.config.name.replace(" ","_") |
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if self.val_size > 0 : |
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return [ |
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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) |
