# Dataset Usage

## Description

The Mimic-IV dataset generate data by executing the Pipeline available on https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.
## 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.
     - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file and in 'features_names' column of the dataset.
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
import datasets
from datasets import load_dataset

# Example 1: Load dataset with default settings
dataset = load_dataset('thbndi/Mimic4Dataset', task="Mortality", mimic_path="/path/to/mimic_data")

# 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.
region:us
{}
TAGS
#region-us 

# Dataset Usage

## Description

The Mimic-IV dataset generate data by executing the Pipeline available on URL
## Function Signature



## 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/URL').
   - 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.
     - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file and in 'features_names' column of the dataset.
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



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.
# Dataset Usage ## Description

The Mimic-IV dataset generate data by executing the Pipeline available on URL ## Function Signature ## 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/URL').
   - 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.
     - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file and in 'features_names' column of the dataset.
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



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.
TAGS
#region-us 
 # Dataset Usage ## Description

The Mimic-IV dataset generate data by executing the Pipeline available on URL ## Function Signature ## 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/URL').
   - 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.
     - For 'concat' and 'aggreg' the composition of the vector is given in './data/dict/"task"/features_aggreg.csv' or './data/dict/"task"/features_concat.csv' file and in 'features_names' column of the dataset.
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



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.