Create README.md

README.md

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+# Dataset Usage
+
+## Description
+
+The `load_dataset` function is a powerful tool to efficiently load and prepare the Mimic-IV dataset for various healthcare analysis tasks. It offers a wide range of options for encoding data and generating cohorts, allowing for seamless integration into your research or application.
+
+## Function Signature
+
+```python
+load_dataset('thbndi/Mimic4Dataset', task, mimic_path=mimic_data, config_path=config_file, encoding=encod, generate_cohort=gen_cohort, val_size=size, cache_dir=cache)
+```
+
+## Arguments
+
+1. `task` (string) :
+   - Description: Specifies the task you want to perform with the dataset.
+   - Default: "Mortality"
+   - Note: Possible Values : 'Phenotype', 'Length of Stay', 'Readmission', 'Mortality'
+
+2. `mimic_path` (string) :
+   - Description: Complete path to the Mimic-IV raw data on user's machine.
+   - Note: You need to provide the appropriate path where the Mimic-IV data is stored.
+
+3. `config_path` (string) optionnal :
+   - Description: Path to the configuration file for the cohort generation choices (more infos in '/config/readme.md').
+   - Default: Configuration file provided in the 'config' folder.
+
+4. `encoding` (string) optionnal :
+   - Description: Data encoding option for the features.
+   - Options: "concat", "aggreg", "tensor", "raw", "text"
+   - Default: "concat"
+   - Note: Choose one of the following options for data encoding:
+     - "concat": Concatenates the one-hot encoded diagnoses, demographic data vector, and dynamic features at each measured time instant, resulting in a high-dimensional feature vector.
+     - "aggreg": Concatenates the one-hot encoded diagnoses, demographic data vector, and dynamic features, where each item_id is replaced by the average of the measured time instants, resulting in a reduced-dimensional feature vector.
+     - "tensor": Represents each feature as an 2D array. There are separate arrays for labels, demographic data ('DEMO'), diagnosis ('COND'), medications ('MEDS'), procedures ('PROC'), chart/lab events ('CHART/LAB'), and output events data ('OUT'). Dynamic features are represented as 2D arrays where each row contains values at a specific time instant.
+     - "raw": Provide cohort from the pipeline without any encoding for custom data processing.
+     - "text": Represents diagnoses as text suitable for BERT or other similar text-based models.
+
+5. `generate_cohort` (bool) optionnal :
+   - Description: Determines whether to generate a new cohort from Mimic-IV data.
+   - Default: True
+   - Note: Set it to True to generate a cohort, or False to skip cohort generation.
+
+6. `val_size`, 'test_size' (float) optionnal :
+   - Description: Proportion of the dataset used for validation during training.
+   - Default: 0.1 for validation size and 0.2 for testing size.
+   - Note: Can be set to 0.
+
+7. `cache_dir` (string) optionnal :
+   - Description: Directory where the processed dataset will be cached.
+   - Note: Providing a cache directory for each encoding type can avoid errors when changing the encoding type.
+
+## Example Usage
+
+```python
+from your_module import load_dataset
+
+# Example 1: Load dataset with default settings
+dataset = load_dataset('thbndi/Mimic4Dataset', task="Mortality")
+
+# Example 2: Load dataset with custom settings
+dataset = load_dataset('thbndi/Mimic4Dataset', task="Phenotype", mimic_path="/path/to/mimic_data", config_path="/path/to/config_file", encoding="aggreg", generate_cohort=False, val_size=0.2, cache_dir="/path/to/cache_dir")
+```
+
+Please note that the provided examples are for illustrative purposes only, and you should adjust the paths and settings based on your actual dataset and specific use case.