trialbench 0.3.0

A multimodal AI-Ready Dataset. Updated Regularly. More details from TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.

TrialBench: Multi-modal AI-ready Clinical Trial Datasets

1. Installation

pip install trialbench

2. Tasks & Phases

Supported Tasks	Task Name	Phase Name
Mortality Prediction	mortality_rate/mortality_rate_yn	1-4
Adverse Event Prediction	serious_adverse_rate/serious_adverse_rate_yn	1-4
Patient Retention Prediction	patient_dropout_rate/patient_dropout_rate_yn	1-4
Trial Duration Prediction	duration	1-4
Trial Outcome Prediction	outcome	1-4
Trial Failure Analysis	failure_reason	1-4
Dosage Prediction	dose/dose_cls	All

Clinical Trial Phases

Phase 1: Safety Evaluation
Phase 2: Efficacy Assessment
Phase 3: Large-scale Testing
Phase 4: Post-marketing Surveillance

3. Quick Start

3.1 Usage of trialbench

import trialbench

# Download all datasets at once (optional)
save_path = 'data/'
trialbench.function.download_all_data(save_path)

# Load dataset
task = 'dose'
phase = 'All'

# Load dataloader.Dataloader 
train_loader, valid_loader, test_loader, num_classes, tabular_input_dim = trialbench.function.load_data(task, phase, data_format='dl')
# or Load pd.Dataframe
train_df, valid_df, test_df, num_classes, tabular_input_dim = trialbench.function.load_data(task, phase, data_format='df')

3.2 Attributes of Each Task

Each task provides different feature sets. The Dosage Prediction task returns nctid_lst, smiles_lst, and mesh_lst, while all other tasks provide nctid_lst, icdcode_lst, smiles_lst, criteria_lst, tabular_lst, text_lst, and mesh_lst.

All tasks use label_lst as the label variable. Please refer to the guide documentation for detailed feature as well as label descriptions.

# Demo for accessing data elements
task = 'dose'
phase = 'All'

# When using DataLoader objects:
# Features
nctid_list = train_loader.dataset.nctid_lst
smiles_list = train_loader.dataset.smiles_lst
mesh_list = train_loader.dataset.mesh_lst
# Labels
# return [datatset_name, label_max, label_min, label_avg], e.g. ['NCT03422510', 2, 2, 2]
label_list = train_loader.dataset.label_lst 

# When using DataFrames:
# Features
nctid_list = train_df.nctid_lst
smiles_list = train_df.smiles_lst
mesh_list = train_df.mesh_lst
# Labels
label_list = train_df.label_lst

4. Data Loading

load_data Parameters

Parameter	Type	Description
task	str	Target prediction task (e.g., 'mortality_rate_yn')
phase	int	Clinical trial phase (1-4)
data_format	str	Data format ('dl' for Dataloader, 'df' for pd.DataFrame)

5. Citation

If you use TrialBench in your research, please cite:

@article{chen2024trialbench,
  title={Trialbench: Multi-modal artificial intelligence-ready clinical trial datasets},
  author={Chen, Jintai and Hu, Yaojun and Wang, Yue and Lu, Yingzhou and Cao, Xu and Lin, Miao and Xu, Hongxia and Wu, Jian and Xiao, Cao and Sun, Jimeng and others},
  journal={arXiv preprint arXiv:2407.00631},
  year={2024}
}