dataset_info:
- config_name: conversational
features:
- name: id
dtype: string
- name: prompt
list:
- name: role
dtype: string
- name: content
dtype: string
- name: completion
list:
- name: role
dtype: string
- name: content
dtype: string
- name: Label
dtype: string
splits:
- name: train
num_bytes: 29687479
num_examples: 8143
- name: dev
num_bytes: 3253954
num_examples: 907
- name: test
num_bytes: 15518665
num_examples: 3427
download_size: 19010599
dataset_size: 48460098
- config_name: processed
features:
- name: id
dtype: string
- name: status
dtype: string
- name: why_stop
dtype: string
- name: Phase
dtype: string
- name: Diseases
dtype: string
- name: ICD_Codes
dtype: string
- name: Drugs
dtype: string
- name: SMILES
dtype: string
- name: CT_Criteria
dtype: string
- name: Label
dtype: string
splits:
- name: train
num_bytes: 24311848
num_examples: 8143
- name: dev
num_bytes: 2644392
num_examples: 907
- name: test
num_bytes: 13752994
num_examples: 3427
download_size: 17699289
dataset_size: 40709234
- config_name: source
features:
- name: nctid
dtype: string
- name: status
dtype: string
- name: why_stop
dtype: string
- name: label
dtype: int64
- name: phase
dtype: string
- name: diseases
dtype: string
- name: icdcodes
dtype: string
- name: drugs
dtype: string
- name: smiless
dtype: string
- name: criteria
dtype: string
splits:
- name: train
num_bytes: 24287418
num_examples: 8143
- name: dev
num_bytes: 2641671
num_examples: 907
- name: test
num_bytes: 13742713
num_examples: 3427
download_size: 17691541
dataset_size: 40671802
configs:
- config_name: conversational
data_files:
- split: train
path: conversational/train-*
- split: dev
path: conversational/dev-*
- split: test
path: conversational/test-*
- config_name: processed
data_files:
- split: train
path: processed/train-*
- split: dev
path: processed/dev-*
- split: test
path: processed/test-*
- config_name: source
data_files:
- split: train
path: source/train-*
- split: dev
path: source/dev-*
- split: test
path: source/test-*
license: cc-by-sa-4.0
task_categories:
- question-answering
- text-classification
language:
- en
tags:
- medical
pretty_name: HINT
size_categories:
- 10K<n<100K
HINT: Hierarchical interaction network for clinical-trial-outcome predictions
Dataset Description
| Links | |
|---|---|
| Homepage: | Github.io |
| Repository: | Github |
| Paper: | arXiv |
| Contact (Original Authors): | Tianfan Fu ([email protected]) |
| Contact (Curator): | Artur Guimarães ([email protected]) |
Dataset Summary
Clinical trials are crucial for drug development but are time consuming, expensive, and often burdensome on patients. More importantly, clinical trials face uncertain outcomes due to issues with efficacy, safety, or problems with patient recruitment. If we were better at predicting the results of clinical trials, we could avoid having to run trials that will inevitably fail more resources could be devoted to trials that are likely to succeed. In this paper, we propose Hierarchical INteraction Network (HINT) for more general, clinical trial outcome predictions for all diseases based on a comprehensive and diverse set of web data including molecule information of the drugs, target disease information, trial protocol and biomedical knowledge. HINT first encode these multi-modal data into latent embeddings, where an imputation module is designed to handle missing data. Next, these embeddings will be fed into the knowledge embedding module to generate knowledge embeddings that are pretrained using external knowledge on pharmaco-kinetic properties and trial risk from the web. Then the interaction graph module will connect all the embedding via domain knowledge to fully capture various trial components and their complex relations as well as their influences on trial outcomes. Finally, HINT learns a dynamic attentive graph neural network to predict trial outcome. Comprehensive experimental results show that HINT achieves strong predictive performance, obtaining 0.772, 0.607, 0.623, 0.703 on PR-AUC for Phase I, II, III, and indication outcome prediction, respectively. It also consistently outperforms the best baseline method by up to 12.4\% on PR-AUC.
Data Instances
{
'TO:DO': ...,
...
}
Data Fields
TO:DO
Additional Information
Dataset Curators
Original Paper
- Tianfan Fu - Georgia Institute of Technology
- Kexin Huang - Harvard University
- Cao Xiao - Analytics Center of Excellence, IQVIA
- Lucas M. - Temple University
- Jimeng Sun - University of Illinois at Urbana-Champaign
Huggingface Curator
- Artur Guimarães ([email protected]) - INESC-ID / University of Lisbon - Instituto Superior Técnico
Licensing Information
Citation Information
@article{fu2021hint,
title={HINT: Hierarchical Interaction Network for Trial Outcome Prediction Leveraging Web Data},
author={Fu, Tianfan and Huang, Kexin and Xiao, Cao and Glass, Lucas M and Sun, Jimeng},
journal={arXiv preprint arXiv:2102.04252},
year={2021}
}
Contributions
Thanks to araag2 for adding this dataset.