README.md

Enhancing Precision Drug Recommendations via In-depth Exploration of Motif Relationships

About Our Work

Update: 2023/11/29: We have created a repository for the paper titled Enhancing Precision Drug Recommendations via In-depth Exploration of Motif Relationships, which has been accepted by the IEEE Transactions on Knowledge and Data Engineering (TKDE). In this repository, we offer the original sample datasets, preprocessing scripts, and algorithm files to showcase the reproducibility of our work.

Requirements

• Python == 3.8.10
• Pytorch == 1.12.0
• rdkit == 2023.3.2
• dgl == 1.13.1
• pandas == 2.0.3
• matplotlib == 3.5.2

Data Sets

The structure of the data set should be like,

data
|_ raw
|  |_ ADDMISSIONS.csv
|  |_ DIAGNOSES_ICD.csv
|  |_ PATIENTS.csv
|  |_ PRESCRIPTIONS.csv
|  |_ PROCEDURES_ICD.csv
|  |_ RXCUIatc4.csv
|  |_ drug-atc.csv
|  |_ drug-DDI.csv
|  |_ drugbank_drugs_info.csv
|  |_ idx2SMILES.pkl
|  |_ ndc2atc_level4.csv
|  |_ ndc2RXCUI.txt
|_ raw2
|  |_ ...

After processing, the structure of the data set should be like,

data
|_ ready
|  |_ atc3toSMILES.pkl
|  |_ ddi_A_final.pkl
|  |_ ddi_mask_H.pkl
|  |_ drug_smile.pkl
|  |_ ehr_adj_final.pkl
|  |_ records_final.pkl
|  |_ smile_sub_b.pkl
|  |_ smile_sub_degree_b.pkl
|  |_ smile_sub_recency_b.pkl
|  |_ smile_sub_voc_b.pkl
|  |_ smile_sub.pkl
|  |_ voc_final.pkl
|_ ready2
|  |_ ...

In accordance with the requirements of the PhysioNet Clinical Databases, we are unable to openly share the data without prior permission. Researchers interested in accessing the data can submit their requests through the following website, and a concise application process can be found here: MIMIC-III and MIMIC-IV.

RUN

# unzip all files into the data directory
cd /root/DrugRec/data
python preprocess.py # preprocess
cd /root/DrugRec/src
python main.py # main file

Contact

Please contact [email protected] if you have any problems.