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Active Learning With Rich feedabck

Project description


Sample code in AL_Notebook.ipynb notebook


  • pip install active_learning
  • OR
  • python sdist
  • python install

Environment Setup

  • Make sure that conda is installed.
  • Run the following command in the root directory to build the conda environment "trews": conda env create -f environment.yml
  • Run source activate trews before executing the Jupyter notebook.
  • "trews" has a package nb_conda which allows you to specify the conda env you want as a Jupyter kernel. You must have "trews" activated for the Jupyter notebook server to manage conda environments


  • Provide justification for custom sepsis definition generated from soliciting rich feedback from physicians.
  • Create an AL implementation that incorporates rich feedback from physicians to improve the TREWS tool.
  • Long-term goal: Create a library of active learning tools for any CDSS we design for new clinical problems.

Code Organization

  • General functions should be written in .py files under folder 'python_scripts'
  • Experiments should load these python files into iPython notebooks for visualization/output neatness and reproducibility.
    • Large experimental datasets should be stored locally and tracked using plaintext files or logs.
  • Datasets used for testing implementation should be kept under repository 'dev_data'

Project details

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