solarsys / clinical-trial-outcome-prediction

clinical trial; clinical trial approval probability prediction; graph neural network; clinical trial dataset; deep neural network; Hierarchical Interaction Network (HINT);

HINT: Hierarchical Interaction Network for Predicting Clinical Trial Approval Probability

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.

Table Of Contents

• Installation
• Benchmark
• HINT: Learn and Inference
• Tutorial (Jupyter Notebook)
• Contact

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Installation

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

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Benchmark

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.

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HINT: Learn and Inference

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.

Tutorial (jupyter notebook)

• 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.

Contact

Please contact [e-mail] for help or submit an issue. This is a joint work with Kexin Huang, Cao(Danica) Xiao, Lucas M. Glass and Jimeng Sun.