trialbench 0.2.0

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

import trialbench

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

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

# Load data
train_loader, valid_loader, test_loader, num_classes, tabular_input_dim = trialbench.load_data(task, phase)

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)

Returns

Object	Type	Description
train_loader	DataLoader	Training set loader
valid_loader	DataLoader	Validation set loader
test_loader	DataLoader	Test set loader
num_classes	int	Number of output classes
tabular_input_dim	int	Dimension of tabular features

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}
}