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