-
The results using the
archived
branch (results of the paper):DDI: 0.0589 (0.0005) Ja: 0.5213 (0.0030) F1: 0.6768 (0.0027) PRAUC: 0.7647 (0.0025)
- Check here https://github.com/ycq091044/SafeDrug/tree/archived for reproducing the paper results.
-
The results using this
master
branch (@J-Zhangg obtained this in issue #23, thanks):# When the learning rate is set to 5e-4: DDI: 0.0632 (0.0003) Ja: 0.5114 (0.0026) F1: 0.6676 (0.0023) PRAUC: 0.7649 (0.0028) # When the learning rate is set to 2e-4: DDI: 0.0607 (0.0005) Ja: 0.5089 (0.0022) F1: 0.6659 (0.0019) PRAUC: 0.7632 (0.0022)
[Implementation difference] Here are two main differences:
- As we mentioned below, the main difference of two branches is in how we get the drug SMILES string (the paper crawl methods misses a lot of molecules, while the current branch uses drugbank method, which gives more comprehensive sets).
- The data processing scripts are also a bit difference, and thus output data statistics differ from the ones reported in the paper.
[which branch to use?] General guidance:
- This
master
branch contains more descriptions (to learn how to use our codes), and the folder structures are very similar toarchived
branch. - Use the
archived
branch to reproduce the results in the paper.
data/
- procesing.py our data preprocessing file.
Input/
(extracted from external resources)- PRESCRIPTIONS.csv: the prescription file from MIMIC-III raw dataset
- DIAGNOSES_ICD.csv: the diagnosis file from MIMIC-III raw dataset
- PROCEDURES_ICD.csv: the procedure file from MIMIC-III raw dataset
- RXCUI2atc4.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/sjy1203/GAMENet, where the name is called ndc2atc_level4.csv.
- 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/sjy1203/GAMENet.
- ndc2RXCUI.txt: NDC to RXCUI mapping file. This file is obtained from https://github.com/sjy1203/GAMENet, where the name is called ndc2rxnorm_mapping.csv.
- drugbank_drugs_info.csv: drug information table downloaded from drugbank here https://drive.google.com/file/d/1EzIlVeiIR6LFtrBnhzAth4fJt6H_ljxk/view?usp=sharing, 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=sharing
Output/
- 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 matrix
- ddi_matrix_H.pkl: H mask structure (This file is created by ddi_mask_H.py)
- ehr_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 (NOTE: we only provide the first 100 entries as examples here. We cannot distribute the whole MIMIC-III data https://physionet.org/content/mimiciii/1.4/, then please download the dataset by yourself and use our processing code to obtain the full records.).
- voc_final.pkl: diag/prod/med index to code dictionary
src/
- SafeDrug.py: our model
- baselines:
- GAMENet.py
- DMNC.py: there are some issues for the latest dnc package, please refer to the original DMNC repo https://github.com/thaihungle/DMNC
- Leap.py
- Retain.py
- ECC.py
- LR.py
- setting file
- model.py
- util.py
- layer.py
Note that we previously use ./data/get_SMILES.py for getting SMILES strings from drugbank. However, due to the web structure change of drugbank, this crawler is not used in the current pipeline. Now, we are using drugbank_drugs_info.csv to obtain the SMILES string for each ATC3 code, thus, the data statistics differ a bit from the paper. The current statistics are shown below:
#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 original PRESCRIPTIONS.csv file provides
ndc->drugname
mapping - Use the ndc2RXCUI.txt file for
ndc->RXCUI
mapping (now we haveRXCUI->drugname
) - Use the RXCUI2atc4.csv file for
RXCUI->atc4
mapping, then changeatc4
toatc3
(now we haveatc3->drugname
) - Use the drugbank_drugs_info.csv file for
drug->SMILES
mapping (now we haveatc3->SMILES
) atc3
is a coarse-granular drug classification, oneatc3
code contains multiple SMILES strings.
ALSO, check out this tool for easy medication code mapping https://github.com/ycq091044/MedCode.
- first, install the rdkit conda environment
conda create -c conda-forge -n SafeDrug rdkit
conda activate SafeDrug
# can also use the following in your current env
pip install rdkit-pypi
- then, in SafeDrug 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)
cd ./data wget -r -N -c -np --user [account] --ask-password https://physionet.org/files/mimiciii/1.4/
-
go into the folder and unzip three main files
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
-
download the DDI file and move it to the data folder download https://drive.google.com/file/d/1mnPc0O0ztz0fkv3HF-dpmBb8PLWsEoDz/view?usp=sharing
mv drug-DDI.csv ./data
-
processing the data to get a complete records_final.pkl
cd ./data vim processing.py # line 323-325 # med_file = './physionet.org/files/mimiciii/1.4/PRESCRIPTIONS.csv' # diag_file = './physionet.org/files/mimiciii/1.4/DIAGNOSES_ICD.csv' # procedure_file = './physionet.org/files/mimiciii/1.4/PROCEDURES_ICD.csv' python processing.py
python SafeDrug.py
here is the argument:
usage: SafeDrug.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
@inproceedings{yang2021safedrug,
title = {SafeDrug: Dual Molecular Graph Encoders for Safe Drug Recommendations},
author = {Yang, Chaoqi and Xiao, Cao and Ma, Fenglong and Glass, Lucas and Sun, Jimeng},
booktitle = {Proceedings of the Thirtieth International Joint Conference on
Artificial Intelligence, {IJCAI} 2021},
year = {2021}
}
Welcome to contact me chaoqiy2@illinois.edu for any question. Partial credit to https://github.com/sjy1203/GAMENet.