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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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_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 : |
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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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_GITHUB = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main" |
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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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mimic_path, |
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**kwargs, |
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): |
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super().__init__(**kwargs) |
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self.mimic_path =mimic_path |
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class Mimic4Dataset(datasets.GeneratorBasedBuilder): |
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VERSION = datasets.Version("1.0.0") |
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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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data_dir=os.path.abspath("./data/dict/cohort_icu_readmission_30_I50"), |
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description="Dataset for mimic4 Phenotype task", |
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mimic_path = None |
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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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data_dir=os.path.abspath("./data/dict"), |
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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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data_dir=os.path.abspath("./data/dict"), |
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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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data_dir=os.path.abspath("./data/dict"), |
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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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"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": datasets.Sequence( |
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{ |
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"signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, |
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"rate" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, |
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"amount" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) } |
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}), |
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"PROC": datasets.Sequence( |
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{datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))} |
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), |
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"CHART": datasets.Sequence( |
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{ |
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"signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, |
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"val" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) } |
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}), |
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"OUT": datasets.Sequence( |
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{datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))} |
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), |
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"label": datasets.ClassLabel(names=["0", "1"]), |
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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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github=_GITHUB, |
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) |
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def _split_generators(self, dl_manager): |
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data_dir = self.config.data_dir + "/dataDic" |
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mimic=self.mimic_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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yield hid, { |
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"gender" : gender, |
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"ethnicity" : eth, |
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"age" : age, |
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"MEDS" : { |
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"signal" : meds_features['signal'], |
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"rate" : meds_features['rate'], |
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"amount" : meds_features['amount'] |
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}, |
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"PROC" : proc_features, |
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"CHART" : { |
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"signal" : chart_features['signal'], |
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"val" : chart_features['val'] |
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}, |
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"OUT" : out_features, |
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"COND" : cond_features, |
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"label" : label |
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} |
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