import csv import json import os import pandas as pd import datasets import pickle #import cohort from .test import print_test _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" _URL = "https://github.com/healthylaife/MIMIC-IV-Data-Pipeline/archive/master.zip" 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", mimic_path = None ), Mimic4DatasetConfig( name="Length of Stay", version=VERSION, data_dir=os.path.abspath("./data/dict"), description="Dataset for mimic4 Length of Stay task", mimic_path = None ), Mimic4DatasetConfig( name="Mortality", version=VERSION, data_dir=os.path.abspath("./data/dict"), description="Dataset for mimic4 Mortality task", mimic_path = None ), ] 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()): data_dir = self.config.data_dir + "/dataDic" #mimic=self.mimic_path data_dir = self.config.data_dir + "/dataDic" path_git = dl_manager.download_and_extract(_URL) path_bench = path_git + "/MIMIC-IV-Data-Pipeline-main" return [ datasets.SplitGenerator(name=datasets.Split.TRAIN, gen_kwargs={"filepath": data_dir, "benchmark": path_bench}), ] def _generate_examples(self, filepath,benchmark): print_test('hello',benchmark) 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 }