This is the official repo for Dual Adaptive Interaction Network (DAI-Net), a simple method for coordinated drug recommendation.
- [2024-7-30]: โญ๏ธ Paper of DAI-Net online (accepted by IEEE JBHI). Check out this link for details.
- [2023-11-29]: ๐๐ Codes released.
torch == 1.8.0+cu111
torch-geometric == 1.0.3
torch-scatter == 2.0.9
torch-sparse == 0.6.12
- first, install the
rdkit
(RDKit: Open-Source Cheminformatics Software) conda environment.
conda create -c conda-forge -n DAI-Net rdkit
conda activate DAI-Net
# can also use the following in your current env
pip install rdkit-pypi
- then, in DAI-Net environment, install the following package
pip install scikit-learn, dill, dnc
Note that torch setup may vary according to GPU hardware. Generally, run the following
pip install torch
If you are using RTX 3090, then plase use the following, which is the right way to make torch work.
python3 -m pip install --user torch==1.8.0+cu111 torchvision==0.9.0+cu111 torchaudio==0.8.0 -f https://download.pytorch.org/whl/torch_stable.html
- Finally, install other packages if necessary
pip install [xxx] # any required package if necessary, maybe do not specify the version, the packages should be compatible with rdkit
Here is a list of reference versions for all package
pandas: 1.3.0
dill: 0.3.4
torch: 1.8.0+cu111
rdkit: 2021.03.4
scikit-learn: 0.24.2
numpy: 1.21.1
Let us know any of the package dependency issue. Please pay special attention to pandas, some report that a high version of pandas would raise error for dill loading.
- Go to https://physionet.org/content/mimiciii/1.4/ to download the MIMIC-III dataset (You may need to get the certificate)
wget -r -N -c -np --user [account] --ask-password https://physionet.org/files/mimiciii/1.4/
- Go into the folder and unzip required three files and copy them to the
~/cs598dl4h-project/data/input/
folder
cd ~/physionet.org/files/mimiciii/1.4
gzip -d PROCEDURES_ICD.csv.gz # procedure information
gzip -d PRESCRIPTIONS.csv.gz # prescription information
gzip -d DIAGNOSES_ICD.csv.gz # diagnosis information
cp PROCEDURES_ICD.csv PRESCRIPTIONS.csv DIAGNOSES_ICD.csv ~/cs598dl4h-project/data/input/
- Download additional files in the
~/cs598dl4h-project/data/input/
folder
cd ~/cs598dl4h-project/data/input/
./get_additional_files.sh
-
Processing the data to get a complete
records_final.pkl
cd ~/cs598dl4h-project/data python processing.py
data/
processing.py
: The data preprocessing file.
input/
PRESCRIPTIONS.csv
: the prescription file from MIMIC-III raw dataset
DIAGNOSES_ICD.csv
: the diagnosis file from MIMIC-III raw datasetPROCEDURES_ICD.csv
: the procedure file from MIMIC-III raw datasetRXCUI2atc4.csv
: this is a NDC-RXCUI-ATC4 mapping file, and we only need the RXCUI to ATC4 mapping. This file is obtained from https://github.com/ycq091044/SafeDrug.drug-atc.csv
: this is a CID-ATC file, which gives the mapping from CID code to detailed ATC code (we will use the prefix of the ATC code latter for aggregation). This file is obtained from https://github.com/ycq091044/SafeDrug.rxnorm2RXCUI.txt
: rxnorm to RXCUI mapping file. This file is obtained from https://github.com/ycq091044/SafeDrug.drugbank_drugs_info.csv
: drug information table downloaded from drugbank here https://www.dropbox.com/s/angoirabxurjljh/drugbank_drugs_info.csv?dl=0, which is used to map drug name to drug SMILES string.drug-DDI.csv
: this a large file, containing the drug DDI information, coded by CID. The file could be downloaded from https://drive.google.com/file/d/1mnPc0O0ztz0fkv3HF-dpmBb8PLWsEoDz/view?usp=sharingoutput/
atc3toSMILES.pkl
: drug ID (we use ATC-3 level code to represent drug ID) to drug SMILES string dict
ddi_A_final.pkl
: ddi adjacency matrixehr_adj_final.pkl
: used in GAMENet baseline (if two drugs appear in one set, then they are connected)records_final.pkl
: The final diagnosis-procedure-medication EHR records of each patient, used for train/val/test split.voc_final.pkl
: diag/prod/med index to code dictionarysrc/
SafeDrug.py
: our model
- baseline models:
GAMENet.py
DMNC.py
Leap.py
Retain.py
ECC.py
LR.py
- setting file
model.py
util.py
layer.py
- analysis file
Result-Analysis.ipynb
dependency.sh
requirements.txt
README.md
After the processing have been done, we get the following statistics:
# patients 6350
# clinical events 15032
# diagnosis 1958
# med 112
# procedure 1430
# avg of diagnoses 10.5089143161256
# avg of medicines 11.647751463544438
# avg of procedures 3.8436668440659925
# avg of vists 2.367244094488189
# max of diagnoses 128
# max of medicines 64
# max of procedures 50
# max of visit 29
The processed data is in the path
\DAI-Net\data
You can also process data with
- MIMIC-III
python processing.py
- MIMIC-IV
python processing_4.py
python DAI-Net.py
here is the argument:
usage: DAI-Net.py [-h] [--Test] [--model_name MODEL_NAME]
[--resume_path RESUME_PATH] [--lr LR]
[--target_ddi TARGET_DDI] [--kp KP] [--dim DIM]
optional arguments:
-h, --help show this help message and exit
--Test test mode
--model_name MODEL_NAME
model name
--resume_path RESUME_PATH
resume path
--lr LR learning rate
--target_ddi TARGET_DDI
target ddi
--kp KP coefficient of P signal
--dim DIM dimension
If you find our project useful, we hope you can star our repo and cite our paper as follows:
@article{zou2024dai,
title={DAI-Net: Dual Adaptive Interaction Network for Coordinated Medication Recommendation},
author={Zou, Xin and He, Xiao and Zheng, Xiao and Zhang, Wei and Chen, Jiajia and Tang, Chang},
journal={IEEE Journal of Biomedical and Health Informatics},
year={2024},
publisher={IEEE}
}
Our work followed the original codes at https://github.com/ycq091044/SafeDrug.