Behavioral Testing of Clinical NLP Models

This repository contains code for testing the behavior of clinical prediction models based on patient letters. For a detailed description of the testing framework see our paper What Do You See in this Patient? Behavioral Testing of Clinical NLP Models.

From an existing test set we create test groups by altering specific tokens in the clinical note. We then analyse the change in predictions which reveals the impact of the mention on the clinical NLP model.


Install requirements: pip install -r requirements.txt

Run, e.g. for diagnosis prediction test on gender, age and ethnicity:

    --test_set_path ./path_to_test_set
    --model_path bvanaken/CORe-clinical-diagnosis-prediction
    --task diagnosis
    --shift_keys gender,age,ethnicity
    --save_dir ./results
    --gpu False
Parameter Description
test_set_path Path to original test set file
model_path Path to model or Huggingface model hub checkpoint
task Current options: diagnosis, mortality
shift_keys Which patient characteristics to test. Current options: age, gender, ethnicity, weight, intersectional (gender + ethnicity)
save_dir Directory to save results, default: “./results”
gpu Whether to use a gpu during inference or not, default: False

Using Non-Transformer models

The framework currently focuses on testing Transformer-based models. However, it is easy to extend it to any other prediction model. To do so, simply create a new class implementing the Predictor interface and add it to the .


  author    = {Betty van Aken and
               Sebastian Herrmann and
               Alexander Löser},
  title     = {What Do You See in this Patient? Behavioral Testing of Clinical NLP Models},
  booktitle = {Bridging the Gap: From Machine Learning Research to Clinical Practice, 
               Research2Clinics Workshop @ NeurIPS 2021},
  year      = {2021}


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