GDCurer: An AI-assisted Drug Dosage Prediction System for Graves' Disease

This repository contains the code for our paper GDCurer: An AI-assisted Drug Dosage Prediction System for Graves' Disease by Haowei Lin, Zhao Chen, Jianhua Zhu, Wenpeng Huang, Ritai Na, Yongkang Qiu, Jing Zhao, Sichen Yin, Xiaodong Li, Rongfu Wang, Jianzhu Ma, Lei Kang.

Quick Links

• Overview
• Requirements
• GDCurer Training
  • Data
  • Standard Training Experiments
  • Lifelong Training Experiments
• GDCurer Web App
• Bugs or Questions?
• Citation

Overview

We propose an AI-assisted drug dosage prediction system for Graves' Disease. GDCurer is capable of predicting the optimal dosage of idonine-131 (I-131) by leveraging the patient's thyroid scintigraph (TS), iodine uptake (IU) information, and the half-life of I-131 (HL). To address the issue of lacking accurate drug dosages in the training data due to the bias introduced by clinicans, a novel machine learning method is developed to exploit the treatment information to correct the bias. Furthermore, GDCurer is designed as a lifelong learner which can continously learn from new clinical data by incorporating the techniques of experience replay (ER) and feature distillation (FD).

Requirements

First, install PyTorch by following the instructions from the official website. To faithfully reproduce our results, please use the correct 1.8 version corresponding to your platforms/CUDA versions. PyTorch version higher than 1.8 should also work. For example, if you use Linux and CUDA11.1 (how to check CUDA version), install PyTorch by the following command,

conda install pytorch==1.8.0 torchvision==0.9.0 cudatoolkit=11.1 -c pytorch -c conda-forge

Then run the following script to install the remaining dependencies,

pip install -r requirements.txt

GDCurer Training

In the following section, we describe how to train GDCurer model by using our code.

Data

Before training and evaluation, please download the dataset from this Google Drive link and save them in the ./data directory.

Standard Training Experiments

Training scripts

We provide an example training script to run standard training of GDCurer. Symply run

CUDA_VISIBLE_DEVICES=${your_cuda_device_id} bash scripts/standard.sh

For the results in the paper, we use Nvidia Tesla A100 GPUs with CUDA 11. Using different types of devices or different versions of CUDA/other software may lead to slightly different performance.

Lifelong Training Experiments

To run lifelong learning experiments with 4 training phases, simply run

CUDA_VISIBLE_DEVICES=${your_cuda_device_id} bash scripts/lifelong.sh

GDCurer Web App

We've built a web app on GDCurer using Django and MySQL. You can build it through the webUI.

Bugs or questions?

If you have any questions related to the code or the paper, feel free to email Haowei. If you encounter any problems when using the code, or want to report a bug, you can open an issue. Please try to specify the problem with details so we can help you better and quicker!

Citation

Please cite our paper if you use GDCurer in your work:

@misc{lin2023gdcurer,
   title={GDCurer: An AI-assisted Drug Dosage Prediction System for Graves' Disease},
   author={Lin, Haowei and Chen, Zhao and Kang, Lei and Ma, Jianzhu},
   year={2023}
}