README.md

Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates

This project evaluates Adverse Drug Effect (ADE) classification models with test cases generated from templates (see examples above). All templates for ADE classification can found in templates_all.csv (and templates_base.csv for base templates only).

Preparation

Create an environment and install relevant libraries.

$ pip install -r requirements.txt

Install checklist separately with pip install checklist.

Model Fine-tuning

Set up the config file for fine-tuning by adapting the arguments in model/setup_finetuner_config.py and running the file. (Or directly adapt the arguments in model/brb.ini or model/xlm.ini instead.)

Fine-tune BioRedditBERT by running

$ python finetune.py --configfile brb.ini

Fine-tune XLMRoBERTa by running

$ python finetune.py --configfile xlm.ini

Extracting Entities

Entities to fill the CheckList templates are extracted from the PsyTAR corpus. Save the PsyTAR corpus as checklist_work/data/PsyTAR_dataset.xlsx. Follow the instructions in checklist_work/entity_extraction/extract_entities.ipynb to extract your own entities from PsyTAR or a different corpus.

Running Tests

In folder checklist_work/:

Run checklist_tests.py for your Huggingface sequence classification model. A customized test suite (checklist_customized.py) is run, which uses part of the original CheckList code.

Run all tests:

$ python checklist_tests.py \
    -- model YOUR_MODEL_PATH \
    --temporal_order \
    --positive_sentiment \
    --beneficial_effect \
    --true_beneficial_effect_gold_label 0 \
    --negation

The Positive Sentiment test will use a ADE fill-ins from a list of less severe ADEs. Deactivate this behavior if needed:

$ python checklist_tests.py \
    --positive_sentiment \
    --mild_ade_source None

Inspect default values for sampling of templates and entities as well as other arguments:

$ python checklist_tests.py -h

Cite

 @misc{macphail2024evaluatingrobustnessadversedrug,
      title={Evaluating the Robustness of Adverse Drug Event Classification Models Using Templates}, 
      author={Dorothea MacPhail and David Harbecke and Lisa Raithel and Sebastian Möller},
      year={2024},
      eprint={2407.02432},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2407.02432} 
}