import csv import json import os import pandas as pd import datasets import pickle #import cohort _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 : """ _HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset" _CITATION = "https://proceedings.mlr.press/v193/gupta22a.html" _GITHUB = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/tree/main" class Mimic4DatasetConfig(datasets.BuilderConfig): """BuilderConfig for Mimic4Dataset.""" def __init__( self, mimic_path, #config, **kwargs, ): super().__init__(**kwargs) self.mimic_path =mimic_path #self.config = config #cohort.task_cohort(self.task,self.mimic_path) class Mimic4Dataset(datasets.GeneratorBasedBuilder): VERSION = datasets.Version("1.0.0") BUILDER_CONFIGS = [ Mimic4DatasetConfig( name="Phenotype", version=VERSION, data_dir=os.path.abspath("./data/dict/cohort_icu_readmission_30_I50"), description="Dataset for mimic4 Phenotype task", mimic_path = None ), Mimic4DatasetConfig( name="Readmission", version=VERSION, data_dir=os.path.abspath("./data/dict"), description="Dataset for mimic4 Readmission task", ), Mimic4DatasetConfig( name="Length of Stay", version=VERSION, data_dir=os.path.abspath("./data/dict"), description="Dataset for mimic4 Length of Stay task", ), Mimic4DatasetConfig( name="Mortality", version=VERSION, data_dir=os.path.abspath("./data/dict"), description="Dataset for mimic4 Mortality task", ), ] DEFAULT_CONFIG_NAME = "Mortality" def _info(self): features = datasets.Features( { "gender": datasets.Value("string"), "ethnicity": datasets.Value("string"), "age": datasets.Value("int32"), "COND": datasets.Sequence(datasets.Value("string")), "MEDS": datasets.Sequence( { "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, "rate" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, "amount" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) } }), "PROC": datasets.Sequence( {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))} ), "CHART": datasets.Sequence( { "signal" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) }, "val" : { datasets.Value("int32") : datasets.Sequence(datasets.Value("int32")) } }), "OUT": datasets.Sequence( {datasets.Value("int32") : datasets.Sequence(datasets.Value("int32"))} ), "label": datasets.ClassLabel(names=["0", "1"]), } ) return datasets.DatasetInfo( description=_DESCRIPTION, features=features, homepage=_HOMEPAGE, citation=_CITATION, github=_GITHUB, ) def _split_generators(self, dl_manager): data_dir = self.config.data_dir + "/dataDic" mimic=self.mimic_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'] yield hid, { "gender" : gender, "ethnicity" : eth, "age" : age, "MEDS" : { "signal" : meds_features['signal'], "rate" : meds_features['rate'], "amount" : meds_features['amount'] }, "PROC" : proc_features, "CHART" : { "signal" : chart_features['signal'], "val" : chart_features['val'] }, "OUT" : out_features, "COND" : cond_features, "label" : label }