This repository hosts HINT, a deep learning based method for clinical trial approval prediction. The repository can be mainly divided into two parts:
benchmark
describes the process of curating benchmark dataset named Trial Approval Prediction (TAP) for clinical trial approval prediction.HINT
is the Hierarchical Interaction Network, a deep learning based method.
The following figure illustrates the pipeline of HINT.
- Installation
- Benchmark
- HINT: Learn and Inference
- Tutorial (Jupyter Notebook)
- Contact
We build conda environment and uses conda
or pip
to install the required packages. See conda.yml
for all the packages.
conda create -n predict_drug_clinical_trial python==3.7
conda activate predict_drug_clinical_trial
conda install -c rdkit rdkit
pip install tqdm scikit-learn
pip install torch
pip install seaborn
pip install icd10-cm
We use following command to activate conda environment.
conda activate predict_drug_clinical_trial
To standardize the clinical trial approval prediction, we create a benchmark dataset for Trial Approval Prediction named TAP, which incorporate rich data components about clinical trials, including drug, disease and protocol (eligibility criteria).
All the scripts are in the folder benchmark
.
Please see benchmark/README.md
for details.
After processing the data, we learn the Hierarchical Interaction Network (HINT) on the following four tasks. The following figure illustrates the pipeline of HINT. All the scripts are available in the folder HINT
.
Please see HINT/README.md
for details.
benchmark
:tutorial_benchmark.ipynb
describes some key components of the data curation process.HINT
:tutorial_HINT.ipynb
is a tutorial to learn and evaluate HINT step by step.
Please contact futianfan@gmail.com for help or submit an issue. This is a joint work with Kexin Huang, Cao(Danica) Xiao, Lucas M. Glass and Jimeng Sun.