Update readme.md

readme.md

@@ -1,165 +1,65 @@
-# Configuration File Instructions
-
-Below are the instructions to create a valid configuration file.
-
-## Configuration File Structure
-
-The configuration file should be in YAML format (.config file) and follow the structure outlined below for **ICU Data**:
-
-```yaml
-disease_label: <disease_label> mandatory only if the prediction task is Phenotype
-timePrediction: <timePrediction>
-timeWindow: <timeWindow>
-timebucket: <timebucket>
-radimp: <radimp>
-predW: <predW>
-diagnosis: <diagnosis>
-output: <output>
-chart: <chart>
-proc: <proc>
-meds: <meds>
-disease_filter: <disease_filter>
-icu_no_icu: <icu_no_icu>
-groupingDiag: <groupingDiag>
-select_diag: <select_diag>
-select_med: <select_med>
-select_proc: <select_proc>
-select_out: <select_out>
-select_chart: <select_chart>
-outlier_removal: <outlier_removal>
-outlier: <outlier>
-left_outlier: <left_outlier>
-```
+# Dataset Usage
 
-The configuration file should be in YAML format (.config file) and follow the structure outlined below for **Non-ICU Data**:
-
-```yaml
-disease_label: <disease_label> mandatory only if the prediction task is Phenotype
-timePrediction: <timePrediction>
-timeWindow: <timeWindow>
-timebucket: <timebucket>
-radimp: <radimp>
-predW: <predW>
-diagnosis: <diagnosis>
-lab: <lab>
-proc: <proc>
-meds: <meds>
-disease_filter: <disease_filter>
-icu_no_icu: <icu_no_icu>
-groupingDiag: <groupingDiag>
-groupingProc: <groupingProc>
-groupingMed: <groupingMed>
-select_diag: <select_diag>
-select_med: <select_med>
-select_proc: <select_proc>
-select_lab: <select_lab>
-outlier_removal: <outlier_removal>
-outlier: <outlier>
-left_outlier: <left_outlier>
-```
+## Description
 
-Replace the `<variable>` placeholders with the corresponding values specific to your use case. Detailed explanations of each variable and their valid values are provided in the next section.
-
-## Variable Definitions and Valid Values
-
-- `disease_label` (string): Specifies the disease label for Phenotype prediction task. Don't provide the line if the task is not Phenotype. Valid values: CAD, Heart Failure, CKD, COPD.
-- `timePrediction` (integer): Specifies the time prediction (days). Valid values depend on the task:
-  - For Phenotype task: 30
-  - For Mortality task: 0
-  - For Length of Stay task: Between 1 and 10 (inclusive)
-  - For Readmission task: Between 10 and 150 (inclusive), multiple of 10
-- `timeWindow` (string): Specifies the time window. Valid values:
-  - For Phenotype or Readmission task: Last X hours (with 24 <= X >= 72)
-  - For Mortality or Length of Stay tasks: First X hours (with 24 <= X >= 72)
-- `timebucket` (integer): Specifies the time bucket. Valid values: Between 1 and 6 (inclusive).
-- `radimp` (string): Specifies the imputation method. Valid values:
-  - No Imputation
-  - forward fill and mean
-  - forward fill and median
-- `predW` (integer): Specifies the prediction window. Valid values depend on the task:
-  - For Phenotype, Length of Stay, or Readmission tasks: 0
-  - For Mortality task: Between 2 and 8 (inclusive)
-- `diagnosis`, `output`, `chart`, `proc`, `meds`, `lab` (boolean): Specifies whether to include each respective feature. Valid values: True or False.
-- `disease_filter` (string): Specifies the disease filter if focusing on a cohort with a specific chronic disease. Valid values:
-  - Heart Failure
-  - COPD
-  - CKD
-  - CAD
-  - No Disease Filter
-- `icu_no_icu` (string): Specifies the dataset type. Valid values: ICU.
-- `groupingDiag` (string): Specifies the grouping ICD option for diagnosis. Valid values:
-  - Convert ICD-9 to ICD-10 and group ICD-10 codes
-  - Keep both ICD-9 and ICD-10 codes
-  - Convert ICD-9 to ICD-10 codes
-- `groupingMed`: Specifies if grouping Medication codes should be done to use Non propietary names : Valid values : Yes, No
-- `groupingProc` : Specifies the ICD codes version to perform grouping for procedures : Valid values :
-  - ICD-9 and ICD-10
-  - ICD-10
-- `select_diag`, `select_med`, `select_proc`, `select_out`, `select_chart`,`select_lab` (boolean): Specifies whether to do features selection as describe in https://github.com/healthylaife/MIMIC-IV-Data-Pipeline benchmark. Valid values: True or False.
-- `outlier_removal` (string): Specifies the outlier removal method. Valid values:
-  - No outlier detection
-  - Impute Outlier (default:98)
-  - Remove outliers (default:98)
-- `outlier` (integer): Specifies the outlier threshold. Valid values: Between 90 and 99 (inclusive).
-- `left_outlier` (integer): Specifies the left outlier threshold. Valid values: Between 0 and 10 (inclusive).
-
-## Example Configuration File
-
-Here's an example of a valid configuration file for **ICU Data**:
-
-```yaml
-disease_label: CAD
-timePrediction: 30
-timeWindow: Last 72 hours
-timebucket: 2
-radimp: forward fill and mean
-predW: 0
-diagnosis: True
-output: True
-chart: True
-proc: True
-meds: True
-disease_filter: No Disease Filter
-icu_no_icu: ICU
-groupingICD: Convert ICD-9 to ICD-10 and group ICD-10 codes
-select_diag: False
-select_med: False
-select_proc: False
-select_out: False
-select_chart: False
-outlier_removal: Impute Outlier (default:98)
-outlier: 98
-left_outlier: 0
-```
-Here's an example of a valid configuration file for **Non-ICU Data**:
-
-```yaml
-timePrediction: 0
-timeWindow: First 48 hours
-timebucket: 2
-radimp: forward fill and mean
-predW: 2
-diagnosis: True
-lab: True
-proc: False
-meds: False
-disease_filter: CKD
-icu_no_icu: ICU
-groupingDiag: Convert ICD-9 to ICD-10 and group ICD-10 codes
-select_diag: False
-select_med: False
-select_proc: False
-select_lab: False
-outlier_removal: Impute Outlier (default:98)
-outlier: 98
-left_outlier: 0
-groupingMed: Yes
-groupingProc: ICD-10
+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)
 ```
 
-Feel free to modify the values to fit your specific requirements.
+## Arguments
+
+1. `task` (string, default="Mortality"):
+   - 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.
 
-## Usage
-Provide the full path of your configuration file while calling the loading dataset function with the parameter config_path=`<path_to_config_file>`.
+4. `encoding` (string, default="concat"):
+   - 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, default=True):
+   - 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):
+   - 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):
+   - 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")
+```
 
-For more understanding of the configuration please refer to https://github.com/healthylaife/MIMIC-IV-Data-Pipeline.
+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.