pat2vec

Converts individual patient data into time interval feature vectors, suitable for filtering and concatenation into a data matrix D for binary classification machine learning tasks.

Example use case 1: I aim to compute the mean of n variables for each unique patient, resulting in a single row representing each patient.

Example use case 2: I intend to generate a monthly time series comprising patient data encompassing biochemistry, demographic details, and textual annotations (MedCat annotations) spanning the last 25 years. Each patient's data begins from a distinct start date (diagnosis date), providing a retrospective view.

Table of Contents

• Requirements
• Features
• Installation
• Usage
• Contributing
• License

Notable requirements:

• CogStack (cogstack_v8_lite) (cogstack_search_methods)
• Elasticsearch
• MedCat https://github.com/CogStack/MedCAT
• Python >=3.10
• Python3.10-venv (for install_pat2vec.py)

See requirements.txt

Features:

• Single patient
• Batch patient
• Cohort search and creation
• Automated random controls
• Modular feature space selection
• Look back
• Look forward
• Individual patient time windows.

Installation

Windows:

1. Clone the repository: cd to gloabl_files

   git clone https://github.com/SamoraHunter/pat2vec.git
   cd pat2vec

   Run the installation script:

   install.bat

2. Add the pat2vec directory to the Python path:

   Before importing pat2vec in your Python script, add the following lines to the script, replacing /path/to/pat2vec with the actual path to the pat2vec directory inside your project:

   import sys
   sys.path.append('/path/to/pat2vec')

3. Import pat2vec in your Python script:

   import pat2vec

Unix/Linux:

Option 1: Install All Requirements Automatically

This option installs pat2vec along with its dependencies, including:

• pat2vec_env (virtual environment)
• snomed_methods
• cogstack_search_methods
• clinical_note_splitter

Before running the installation, ensure you:

• Place the model pack in the appropriate directory gloabl_files/medcat_models/%modelpack%.zip
• Populate the credentials file under gloabl_files/credentials.py
• (Optional) Add a SNOMED file if needed gloabl_files/.. 'snomed', 'SnomedCT_InternationalRF2_PRODUCTION_20231101T120000Z', 'SnomedCT_InternationalRF2_PRODUCTION_20231101T120000Z', 'Full', 'Terminology', 'sct2_StatedRelationship_Full_INT_20231101.txt'

Installation Steps:

1. Copy the install_pat2vec.sh file to your installation directory.

2. Grant execution permissions:

   chmod +x install_pat2vec.sh

3. Run the installation using one of the following options:

   • Standard installation:

     ./install_pat2vec.sh

   • Installation with proxy mirror support:

     ./install_pat2vec.sh --proxy

   • Install to a specific directory:

     ./install_pat2vec.sh --directory /path/to/install

   • Skip cloning repositories (if already cloned manually):

     ./install_pat2vec.sh --no-clone

Repositories Installed by This Script:

The script will clone the following repositories:

• pat2vec
• snomed_methods
• cogstack_search_methods
• clinical_note_splitter

---

Option 2: Manual Installation

1. Clone the repository:

   git clone https://github.com/SamoraHunter/pat2vec.git

   . Run the installation script:

   (Requires python3 on path and venv)
   chmod +x install.sh
   ./install.sh

   cd pat2vec

2. Add the pat2vec directory to the Python path:

   Before importing pat2vec in your Python script, add the following lines to the script, replacing /path/to/pat2vec with the actual path to the pat2vec directory inside your project:

   import sys
   sys.path.append('/path/to/pat2vec')

3. Import pat2vec in your Python script:

   import pat2vec

Usage:

• Set paths, gloabl_files/medcat_models/modelpack.zip, gloabl_files/snomed_methods, gloabl_files/..

• gloabl_files/

  • medcat_models/
    • modelpack.zip
  • snomed_methods/snomed_methods_v1.py**
  • pat2vec/
  • pat2vec_projects/
    • project_01/
      • example_usage.ipynb
      • treatment_docs.csv

*treatment_docs.csv should contain a column 'client_idcode' with your UUID's. **https://github.com/SamoraHunter/SNOMED_methods.git

• Configure options

• Run all

• Examine example_usage.ipynb for additional functionality and use cases.

• open example_usage.ipynb and hit run all.

• If testing in a live environment ensure the testing flag is set to False in the config_obj.

Contributing

Contributions are welcome! Please see the contributing guidelines for more information.

License

This project is licensed under the MIT License - see the LICENSE file for details