Pre-training of Graph Augmented Transformers for Medication Recommendation

Last update: Dec 27, 2022

Related tags

Overview

G-Bert

Pre-training of Graph Augmented Transformers for Medication Recommendation

Intro

G-Bert combined the power of Graph Neural Networks and BERT (Bidirectional Encoder Representations from Transformers) for medical code representation and medication recommendation. We use the graph neural networks (GNNs) to represent the structure information of medical codes from a medical ontology. Then we integrate the GNN representation into a transformer-based visit encoder and pre-train it on single-visit EHR data. The pre-trained visit encoder and representation can be fine-tuned for downstream medical prediction tasks. Our model is the first to bring the language model pre-training schema into the healthcare domain and it achieved state-of-the-art performance on the medication recommendation task.

Requirements

pytorch>=0.4
python>=3.5
torch_geometric==1.0.3

Guide

We list the structure of this repo as follows:

.
├── [4.0K]  code/
│   ├── [ 13K]  bert_models.py % transformer models
│   ├── [5.9K]  build_tree.py % build ontology
│   ├── [4.3K]  config.py % hyperparameters for G-Bert
│   ├── [ 11K]  graph_models.py % GAT models
│   ├── [   0]  __init__.py
│   ├── [9.8K]  predictive_models.py % G-Bert models
│   ├── [ 721]  run_alternative.sh % script to train G-Bert
│   ├── [ 19K]  run_gbert.py % fine tune G-Bert
│   ├── [ 19K]  run_gbert_side.py
│   ├── [ 18K]  run_pretraining.py % pre-train G-Bert
│   ├── [4.4K]  run_tsne.py # output % save embedding for tsne visualization
│   └── [4.7K]  utils.py
├── [4.0K]  data/
│   ├── [4.9M]  data-multi-side.pkl 
│   ├── [3.6M]  data-multi-visit.pkl % patients data with multi-visit
│   ├── [4.3M]  data-single-visit.pkl % patients data with singe-visit
│   ├── [ 11K]  dx-vocab-multi.txt % diagnosis codes vocabulary in multi-visit data
│   ├── [ 11K]  dx-vocab.txt % diagnosis codes vocabulary in all data
│   ├── [ 29K]  EDA.ipynb % jupyter version to preprocess data
│   ├── [ 18K]  EDA.py % python version to preprocess data
│   ├── [6.2K]  eval-id.txt % validation data ids
│   ├── [6.9K]  px-vocab-multi.txt % procedure codes vocabulary in multi-visit data
│   ├── [ 725]  rx-vocab-multi.txt % medication codes vocabulary in multi-visit data
│   ├── [2.6K]  rx-vocab.txt % medication codes vocabulary in all data
│   ├── [6.2K]  test-id.txt % test data ids
│   └── [ 23K]  train-id.txt % train data ids
└── [4.0K]  saved/
    └── [4.0K]  GBert-predict/ % model files to reproduce our result
        ├── [ 371]  bert_config.json 
        └── [ 12M]  pytorch_model.bin

Preprocessing Data

We have released the preprocessing codes named data/EDA.ipynb to process data using raw files from MIMIC-III dataset. You can download data files from MIMIC and get necessary mapping files from GAMENet.

Quick Test

To validate the performance of G-Bert, you can run the following script since we have provided the trained model binary file and well-preprocessed data.

cd code/
python run_gbert.py --model_name GBert-predict --use_pretrain --pretrain_dir ../saved/GBert-predict --graph

Cite

Please cite our paper if you find this code helpful:

@article{shang2019pre,
  title={Pre-training of Graph Augmented Transformers for Medication Recommendation},
  author={Shang, Junyuan and Ma, Tengfei and Xiao, Cao and Sun, Jimeng},
  journal={arXiv preprint arXiv:1906.00346},
  year={2019}
}

Acknowledgement

Many thanks to the open source repositories and libraries to speed up our coding progress.

Pre-training of Graph Augmented Transformers for Medication Recommendation

Related tags

Overview

G-Bert

Intro

Requirements

Guide

Preprocessing Data

Quick Test

Cite

Acknowledgement

Owner

PyTorch implementation of our ICCV paper DeFRCN: Decoupled Faster R-CNN for Few-Shot Object Detection.

A repository for storing njxzc final exam review material

We present a regularized self-labeling approach to improve the generalization and robustness properties of fine-tuning.

MTCNN face detection implementation for TensorFlow, as a PIP package.

Preparation material for Dropbox interviews

Efficient neural networks for analog audio effect modeling

Discretized Integrated Gradients for Explaining Language Models (EMNLP 2021)

A rule learning algorithm for the deduction of syndrome definitions from time series data.

RANZCR-CLiP 7th Place Solution

Pytorch for Segmentation

A 2D Visual Localization Framework based on Essential Matrices [ICRA2020]

This repository comes with the paper "On the Robustness of Counterfactual Explanations to Adverse Perturbations"

Discord bot-CTFD-Thread-Parser - Discord bot CTFD-Thread-Parser

Object detection, 3D detection, and pose estimation using center point detection:

[ICCV 2021] Deep Hough Voting for Robust Global Registration

Translate darknet to tensorflow. Load trained weights, retrain/fine-tune using tensorflow, export constant graph def to mobile devices

This is an easy python software which allows to sort images with faces by gender and after by age.

Understanding the Generalization Benefit of Model Invariance from a Data Perspective

UnivNet: A Neural Vocoder with Multi-Resolution Spectrogram Discriminators for High-Fidelity Waveform Generation

Python SDK for building, training, and deploying ML models