Code used for the manuscript "Efficient Joint Learning for Clinical Named Entity Recognition and Relation Extraction Using Fourier Networks: A Use Case in Adverse Drug Events".

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Developed with

• Operating System: Ubuntu 20.04.4 LTS
  • Kernel: Linux 5.4.0-110-generic
  • Architecture: x86-64
• Python:
  • 3.9.7

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Prerequisites

Installation of required libraries

You can install external libraries by running:

# Create the environment.
conda create -n JNRF python=3.9.7

# Activate the environment.
conda activate JNRF

# Go to the project root folder.
cd <path to the project root folder>

# Install external libraries.
pip install -r requirements.txt

# Do stuff: See the "Typical pipeline" section.

# Deactivate the environment.
conda deactivate

# If you wish, you can remove the environment.
conda env remove --name JNRF

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Structure

├── configs
│   ├── inference_config.json (configuration required for inference)
│   ├── preprocessing_config.json (configuration required for preprocessing)
│   └── training_config.json (configuration required for training)
│
├── data
│   ├── loop_pred (folder required for evaluation during training)
│   ├── predictions (folder required to store prediction file before evaluation)
│   ├── test (folder with original raw test data .txt and .ann files)
│   └── train (folder with original raw train data .txt and .ann files)
│
├── runs (folder for tensorboard logs)
│
├── saved_models (folder to save best model during training)
│
├── scr (source code)
│   ├── dataloader_helper.py
│   ├── evaluation_script.py
│   ├── JNRF.py
│   ├── loss.py
│   ├── postprocessing_helper.py
│   └── preprocessing_helper.py
│
├── 1_preprocessing.py (used to preprocess raw data, see configs/preprocessing_config.json for configuration)
│
├── 2_training.py (used to train a JNRF model, see configs/training_config.json for configuration)
│
├── 3_inference.py (used to predict with JNRF a model, see configs/inference_config.json for configuration)
│
├── 4_evaluation.py (used to compute evaluation metrics on the model used in the inference step)
│
└── requirements.txt (used to install dependencies)

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Typical pipeline

Access to data

Apply and download data. Then, put orginial raw test data (.txt and .ann files) in "data/test", and orginial raw train data (.txt and .ann files) in "data/train".

Download Spacy English pipeline

python -m spacy download en_core_web_sm

Step 1: Preprocessing

see configs/preprocessing_config.json for configuration, then:

python 1_preprocessing.py

Step 2: Model training

see configs/training_config.json for configuration. Make sure to change the name of the model to your convenience, then:

python 2_training.py

Step 3: Model prediction

configs/inference_config.json for configuration. Make sure to select the correct model name, then:

python 3_inference.py

Step 4: Evaluation

python 4_evaluation.py

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