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Requirement

  • python env: 3.8+
  • use pip install -r requirements.txt to install dependencies

Models

SDoH_NLPend2end System

  • The system aims for extract SDoH information from clinical notes
  • We support text format for production and brat format for evaluation
  • The system is a two stage pipeline
    • The first stage is to extract SDoH concepts
    • The second stage is to identify relations between extracted concepts

Usage

  • download the models and unzip into this project root directory, you should have:
    • ./models/ner_bert
    • ./models/re_bert
  • then, cd to the ./scripts directory
  • execute pipeline as
bash run_pred.sh -i <input data directory> -c gpu_id
  • "input data directory" is the location of the data you annotated (*.txt and *.ann) e.g., ./test_data
  • gpu_id is the id where you want to run the program. e.g, 0 - use the GPU with id as 0
  • if GPU is not available, try -1 to use CPU which is slow but should work.

Results

  • in the main directory (./SDoH_NLPend2end), we will create three directories for outputs
  • the first is ./logs which saves all the running logs
  • the second is ./temp which saves all the intermediate generated files
  • the third is ./results where the eval_results.txt stores the final performance measurement and the rest directories are the e2e outputs

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