This repository is a PyTorch implementation of our NeurIPS 2022 paper "Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records".
We recommend installing these key packages with the following command.
pip install torch # version >= '1.10.1+cu113'
pip install nltk
pip install transformers
pip install scikit-learn==0.24.2
conda install -c conda-forge rdkit
cd ./data/mimic-iii/fariseq && pip install ./
Other packages can generally be installed with pip or conda command.
Get the certificate first, and then download the MIMIC-III and MIMIC-IV datasets.
- MIMIC-III: https://physionet.org/content/mimiciii/1.4/
- MIMIC-IV: https://physionet.org/content/mimiciv/
Get the following input files from https://github.com/ycq091044/SafeDrug and put them in the folder ./data/input/.
- RXCUI2atc4.csv: NDC-RXCUI-ATC4 mapping
- rxnorm2RXCUI.txt: NDC-RXCUI mapping
- drugbank_drugs_info.csv: Drugname-SMILES mapping
- drug-atc.csv: CID-ATC mapping
- drug-DDI.csv: DDI information coded by CID
Taking MIMIC-III as an example, its data processing includes the following five steps. All generated files will be placed in the output folder ./data/mimic-iii/output/.
After downloading the raw dataset, put these required files in the folder path: ./data/mimic-iii/.
- DIAGNOSES_ICD.csv, PROCEDURES_ICD.csv, PRESCRIPTIONS.csv (diagnosis, procedure, prescription information)
- D_ICD_DIAGNOSES.csv, D_ICD_PROCEDURES.csv (dictionary tables for diagnosis and procedure)
- NOTEEVENTS.csv, ADMISSIONS.csv (used for symptom extraction)
Run processing_iii.py to generate data_iii_sym0.pkl, the merged data frame without symptom information.
cd ./data/mimic-iii/
python processing_iii.py
Run sym_tagger_iii.py to generate data_iii_sym1_mulvisit.pkl by extracting symptom information from clinical notes and admission tables, and removing the patients with only a single visit or no symptom.
python sym_tagger_iii.py
We employ the Clinical BERT (https://github.com/EmilyAlsentzer/clinicalBERT) to tokenize the text of symptoms, diagnoses, and procedures by running input_ids_sdp_iii.py.
We employ a roberta-large based pre-trained model (https://github.com/microsoft/DVMP, checkpoints need to be registered by their authors) to tokenize SMILES strings of medications and encode them by running input_smiles_iii.py.
python input_ids_sdp_iii.py
python input_smiles_iii.py
Run sym_info_iii.py to generate additional symptom information used for the input of model training and inference.
python sym_info_iii.py
Generate records_final_iii.pkl and ddi_A_iii.pkl by running gen_records_ddi.py.
python gen_records_ddi.py
Here is the key argument:
usage: main.py [--Test] [--model_name MODEL_NAME]
[--resume_path RESUME_PATH] [--lr LR]
[--target_ddi TARGET_DDI] [--kp KP] [--dim DIM]
[--CI] [--multivisit]
optional arguments:
--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
--CI causal inference (ATE) loss
--mulitivisit multi visit or single visit
To train a DrugRec model from scratch, run the following command.
python main.py --model_name [YOUR_MODEL_NAME]
To evaluate an existing DrugRec model, you can place the model to the corresponding resume_path and run the following command.
python main.py --Test --model_name [YOUR_MODEL_NAME] --resume_path [YOUR_RESUME_PATH]
@inproceedings{sun2022debiased,
title={Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records},
author={Sun, Hongda and Xie, Shufang and Li, Shuqi and Chen, Yuhan and Wen, Ji-Rong and Yan, Rui},
booktitle={Advances in Neural Information Processing Systems},
year={2022}
}Feel free to contact me [email protected] for any question.
Partial credit to previous reprostories: