DeepIPW

1. Introduction

A computational framework for drug repurposing from real-world data. DeepIPW: Deep Inverse Propensity Weighting.

2. System requirement

OS: Ubuntu 16.04

GPU: NVIDIA 1080ti (11GB memory) is minimum requirement. We recommend NVIDIA TITAN RTX 6000 GPUs.

3. Dependencies

Python 3.6
Pytorch 1.2.0
Scipy 1.3.1
Numpy 1.17.2
Scikit-learn 0.22.2

4. Preprocessing data

Running example

cd preprocess
python run_preprocess.py

Parameters

• --min_patients, minimum number of patients for each cohort.
• --min_prescription, minimum times of prescriptions of each drug.
• --time_interval, minimum time interval for every two prescriptions.
• --followup, number of days of followup period.
• --baseline, number of days of baseline period.
• --input_pickles, data pickles.
• --save_cohort_all, save path.

5. DeepIPW model

Bash command

bash run_lstm.sh

Python command

cd deep-ipw
python main.py

Parameters

• --data_dir, input cohort data
• --pickles_dir, pickles file.
• --treated_drug_file, current evaluating drug.
• --controlled_drug, sampled controlled drugs (randomly sampling or ATC class).
• --controlled_drug_ratio, ratio of the number of controlled drug.
• --input_pickles, data pickles.
• --random_seed.
• --batch_size.
• --diag_emb_size.
• --med_emb_size.
• --med_hidden_size.
• --diag_hidden_size.
• --learning_rate.
• --weight_decay.
• --epochs
• --save_model_filename.
• --outputs_lstm.
• --outputs_lr.
• --save_db.