Metadata-Version: 2.4
Name: trialbench
Version: 0.1.0.dev1
Summary: A multimodal AI-Ready Dataset. Updated Regularly. More details from TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.
Home-page: https://github.com/ML2Health/ML2ClinicalTrials/tree/main
Author: authors of TrialBench: Multi-Modal AI-Ready Datasets for Clinical Trial Prediction.
License: MIT
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: Implementation :: CPython
Classifier: Programming Language :: Python :: Implementation :: PyPy
Requires-Python: >=3.6.0
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20
Requires-Dist: scikit-learn>=1.2
Requires-Dist: pandas>=1.3
Dynamic: author
Dynamic: classifier
Dynamic: description
Dynamic: description-content-type
Dynamic: home-page
Dynamic: license
Dynamic: license-file
Dynamic: requires-dist
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Dynamic: summary


TrialBench: Multi-modal AI-ready Clinical Trial Datasets
====================================

[![PyPI version](https://img.shields.io/pypi/v/trialbench.svg?color=brightgreen)](https://pypi.org/project/trialbench/)

[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

# 1. Installation :rocket:

`` `bash pip install trialbench ``\`

# 2. Tasks & Phases :clipboard:

Supported Tasks \| Task Type \| Task Name \| Phase Name \|
\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\-\--
\| Mortality Prediction \|
[mortality_rate]{.title-ref}/[mortality_rate_yn]{.title-ref} \| 1-4 \|
\| Adverse Event Prediction \|
[serious_adverse_rate]{.title-ref}/[serious_adverse_rate_yn]{.title-ref}
\| 1-4 \| \| Patient Retention Prediction \|
[patient_dropout_rate]{.title-ref}/[patient_dropout_rate_yn]{.title-ref}
\| 1-4 \| \| Trial Duration Prediction \| [duration]{.title-ref} \| 1-4
\| \| Trial Outcome Prediction \| [outcome]{.title-ref} \| 1-4 \| \|
Trial Failure Analysis \| [failure_reason]{.title-ref} \| 1-4 \| \|
Dosage Prediction \| [dose]{.title-ref}/[dose_cls]{.title-ref} \| 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 :zap:

`` `python import trialbench  # 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 :card_file_box:

[load_data]{.title-ref} Parameters \| Parameter \| Type \| Description
\| \-\-\-\-\-- \| [task]{.title-ref} \| str \| Target prediction task
(e.g., \'mortality_rate_yn\') \| \| [phase]{.title-ref} \| int \|
Clinical trial phase (1-4) \|

Returns \| Object \| Type \| Description \|
\-\-\-\-\-\-\-\-\-\-\-\-\-\-- \| [train_loader]{.title-ref} \|
DataLoader \| Training set loader \| \| [valid_loader]{.title-ref} \|
DataLoader \| Validation set loader \| \| [test_loader]{.title-ref} \|
DataLoader \| Test set loader \| \| [num_classes]{.title-ref} \| int \|
Number of output classes \| \| [tabular_input_dim]{.title-ref} \| int \|
Dimension of tabular features \|

# 5. Citation :handshake:

If you use TrialBench in your research, please cite:
`` `bibtex @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} } ``\`

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