Create Mimic4Dataset.py

Mimic4Dataset.py

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+import csv
+import json
+import os
+import pandas as pd
+import datasets
+
+_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'
+#TODO ADD DESCRIPTION COHORTS
+"""
+
+_HOMEPAGE = "https://huggingface.co/datasets/thbndi/Mimic4Dataset"
+_CITATION = "https://proceedings.mlr.press/v193/gupta22a.html"
+
+class Mimic4Dataset(datasets.GeneratorBasedBuilder):
+    VERSION = datasets.Version("1.0.0")
+
+    BUILDER_CONFIGS = [
+        datasets.BuilderConfig(
+            name="Phenotype",
+            version=VERSION,
+            data_dir=os.path.abspath("./data/csv/Phenotype"),
+            description="Dataset for mimic4 Phenotype task",
+        ),
+        datasets.BuilderConfig(
+            name="Readmission",
+            version=VERSION,
+            data_dir=os.path.abspath("./data/csv/Readmission"),
+            description="Dataset for mimic4 Readmission task",
+        ),
+        datasets.BuilderConfig(
+            name="Length of Stay",
+            version=VERSION,
+            data_dir=os.path.abspath("./data/csv/Lenght_of_Stay"),
+            description="Dataset for mimic4 Length of Stay task",
+        ),
+        datasets.BuilderConfig(
+            name="Mortality",
+            version=VERSION,
+            data_dir=os.path.abspath("./data/csv/Mortality"),
+            description="Dataset for mimic4 Mortality task",
+        ),
+    ]
+
+    DEFAULT_CONFIG_NAME = "Mortality"
+
+    def _info(self):
+        
+
+        features = datasets.Features(
+            {
+                "gender": datasets.Value("string"),
+                "ethnicity": datasets.Value("string"),
+                "insurance": datasets.Value("string"),
+                "age": datasets.Value("int32"),
+                "COND": datasets.Sequence(datasets.Value("int32"), length=None),
+                "MEDS": datasets.Sequence(datasets.Value("int32"), length=None),
+                "PROC": datasets.Sequence(datasets.Value("int32"), length=None),
+                "CHART": datasets.Sequence(datasets.Value("int32"), length=None),
+                "OUT": datasets.Sequence(datasets.Value("int32"), length=None),
+                "label": datasets.ClassLabel(names=["0", "1"]),
+            }
+        )
+        return datasets.DatasetInfo(
+            description=_DESCRIPTION,
+            features=features,
+            homepage=_HOMEPAGE,
+            citation=_CITATION,
+        )
+
+    def _split_generators(self, dl_manager):
+        data_dir = self.config.data_dir
+
+        # Collect file paths for all CSV files in the subfolders
+        train_files = []
+        for split_name in os.listdir(data_dir):
+            split_dir = os.path.join(data_dir, split_name)
+            if os.path.isdir(split_dir):
+                for file_name in os.listdir(split_dir):
+                    if file_name.endswith(".csv"):
+                        file_path = os.path.join(split_dir, file_name)
+                        train_files.append(file_path)
+
+        # Return a single SplitGenerator for the train split
+        return [
+            datasets.SplitGenerator(
+                name=datasets.Split.TRAIN,
+                gen_kwargs={
+                    "filepaths": train_files,
+                    "split": datasets.Split.TRAIN,
+                },
+            )
+        ]
+
+
+    def _generate_examples(self, filepaths, split):
+        #each 3 successive files are the same admission (demographic, static, dynamic)
+        labels = pd.read_csv("./data/csv/"+self.config.name +"labels.csv")
+        for i in range(0, len(filepaths), 3):
+            file1, file2, file3 = filepaths[i:i+3]
+            static_file = file1 if "static.csv" in file1 else file2 if "static.csv" in file2 else file3 
+            demographic_file = file1 if "demo.csv" in file1 else file2 if "demo.csv" in file2 else file3
+            dynamic_file = file1 if "dynamic.csv" in file1 else file2 if "dynamic.csv" in file2 else file3 
+            
+            #dynamic
+            dyn = pd.read_csv(dynamic_file, header=[0, 1])
+            meds = dyn['MEDS']
+            proc = dyn['PROC']
+            chart = dyn['CHART']
+            out = dyn['OUT']
+            
+            #static
+            stat = pd.read_csv(static_file, header=[0, 1])
+            stat = stat['COND']
+            
+            #demo
+            demo = pd.read_csv(demographic_file, header=0)
+
+            #dict
+            stat_dict = stat.iloc[0].to_dict()
+            demo_dict = demo.iloc[0].to_dict()
+            meds_dict = meds.iloc[0].to_dict()
+            proc_dict = proc.iloc[0].to_dict()
+            chart_dict = chart.iloc[0].to_dict()
+            out_dict = out.iloc[0].to_dict()
+
+            #get stay_id which is the name of the folder containing the files
+            stay_id = demographic_file.split("/")[-2]
+            #get the label
+            label = int(labels.loc[labels['stay_id'] == stay_id]['label'])
+
+            yield stay_id, {
+                "gender" : demo_dict['gender'],
+                "ethnicity" : demo_dict['ethnicity'],
+                "insurance" : demo_dict['insurance'],
+                "age" : demo_dict['age'],
+                "MEDS" : meds_dict,
+                "PROC" : proc_dict,
+                "CHART" : chart_dict,
+                "OUT" : out_dict,
+                "COND" : stat_dict,
+                "label" : label
+            }