Dataset for the Research2Clinics @ NeurIPS 2021 Paper: What Do You See in this Patient? Behavioral Testing of Clinical NLP Models

Overview

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.

Usage

Install requirements: pip install -r requirements.txt

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

python main.py 
    --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 TASK_MAP in main.py.

Cite

@inproceedings{vanAken2021,
  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}
}
Owner
Betty van Aken
PhD student at Beuth University of Applied Sciences in Berlin doing research in Clinical NLP & Explainability
Betty van Aken
Use deep learning, genetic programming and other methods to predict stock and market movements

StockPredictions Use classic tricks, neural networks, deep learning, genetic programming and other methods to predict stock and market movements. Both

Linda MacPhee-Cobb 386 Jan 03, 2023
Implementation of H-Transformer-1D, Hierarchical Attention for Sequence Learning

H-Transformer-1D Implementation of H-Transformer-1D, Transformer using hierarchical Attention for sequence learning with subquadratic costs. For now,

Phil Wang 123 Nov 17, 2022
An implementation of the efficient attention module.

Efficient Attention An implementation of the efficient attention module. Description Efficient attention is an attention mechanism that substantially

Shen Zhuoran 194 Dec 15, 2022
Code for KDD'20 "An Efficient Neighborhood-based Interaction Model for Recommendation on Heterogeneous Graph"

Heterogeneous INteract and aggreGatE (GraphHINGE) This is a pytorch implementation of GraphHINGE model. This is the experiment code in the following w

Jinjiarui 69 Nov 24, 2022
Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation

Tiny-NewsRec The source codes for our paper "Tiny-NewsRec: Efficient and Effective PLM-based News Recommendation". Requirements PyTorch == 1.6.0 Tensor

Yang Yu 3 Dec 07, 2022
Learning Dense Representations of Phrases at Scale (Lee et al., 2020)

DensePhrases DensePhrases provides answers to your natural language questions from the entire Wikipedia in real-time. While it efficiently searches th

Princeton Natural Language Processing 540 Dec 30, 2022
Source code of our BMVC 2021 paper: AniFormer: Data-driven 3D Animation with Transformer

AniFormer This is the PyTorch implementation of our BMVC 2021 paper AniFormer: Data-driven 3D Animation with Transformer. Haoyu Chen, Hao Tang, Nicu S

24 Nov 02, 2022
Pytorch implementation of RED-SDS (NeurIPS 2021).

Recurrent Explicit Duration Switching Dynamical Systems (RED-SDS) This repository contains a reference implementation of RED-SDS, a non-linear state s

Abdul Fatir 10 Dec 02, 2022
Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal, multi-exposure and multi-focus image fusion.

U2Fusion Code of U2Fusion: a unified unsupervised image fusion network for multiple image fusion tasks, including multi-modal (VIS-IR, medical), multi

Han Xu 129 Dec 11, 2022
RealTime Emotion Recognizer for Machine Learning Study Jam's demo

Emotion recognizer Table of contents Clone project Dataset Install dependencies Main program Demo 1. Clone project git clone https://github.com/GDSC20

Google Developer Student Club - UIT 1 Oct 05, 2021
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking

One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking This is an official implementation for NEAS presented in CVPR

Multimedia Research 19 Sep 08, 2022
A transformer-based method for Healthcare Image Captioning in Vietnamese

vieCap4H Challenge 2021: A transformer-based method for Healthcare Image Captioning in Vietnamese This repo GitHub contains our solution for vieCap4H

Doanh B C 4 May 05, 2022
Sleep staging from ECG, assisted with EEG

Sleep_Staging_Knowledge Distillation This codebase implements knowledge distillation approach for ECG based sleep staging assisted by EEG based sleep

2 Dec 12, 2022
Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance

Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance Project Page | Paper | Data This repository contains an implementatio

Lior Yariv 521 Dec 30, 2022
LSTM Neural Networks for Spectroscopic Studies of Type Ia Supernovae

Package Description The difficulties in acquiring spectroscopic data have been a major challenge for supernova surveys. snlstm is developed to provide

7 Oct 11, 2022
ICRA 2021 - Robust Place Recognition using an Imaging Lidar

Robust Place Recognition using an Imaging Lidar A place recognition package using high-resolution imaging lidar. For best performance, a lidar equippe

Tixiao Shan 293 Dec 27, 2022
Architecture Patterns with Python (TDD, DDD, EDM)

architecture-traning Architecture Patterns with Python (TDD, DDD, EDM) Chapter 5. 높은 기어비와 낮은 기어비의 TDD 5.2 도메인 계층 테스트를 서비스 계층으로 옮겨야 하는가? 도메인 계층 테스트 def

minsung sim 2 Mar 04, 2022
[NeurIPS 2021] Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods

Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods Large Scale Learning on Non-Homophilous Graphs: New Benchmark

60 Jan 03, 2023
Runtime type annotations for the shape, dtype etc. of PyTorch Tensors.

torchtyping Type annotations for a tensor's shape, dtype, names, ... Turn this: def batch_outer_product(x: torch.Tensor, y: torch.Tensor) - torch.Ten

Patrick Kidger 1.2k Jan 03, 2023
The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting".

IGMTF The source code and data of the paper "Instance-wise Graph-based Framework for Multivariate Time Series Forecasting". Requirements The framework

Wentao Xu 24 Dec 05, 2022