Summary of Trialbench: Multi-modal Artificial Intelligence-ready Clinical Trial Datasets, by Jintai Chen et al.
TrialBench: Multi-Modal Artificial Intelligence-Ready Clinical Trial Datasets
by Jintai Chen, Yaojun Hu, Yue Wang, Yingzhou Lu, Xu Cao, Miao Lin, Hongxia Xu, Jian Wu, Cao Xiao, Jimeng Sun, Lucas Glass, Kexin Huang, Marinka Zitnik, Tianfan Fu
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenges of applying artificial intelligence (AI) to forecast and simulate key events in clinical trials. They present a comprehensive suite of meticulously curated AI-ready datasets covering multi-modal data and 8 crucial prediction challenges in clinical trial design. The datasets include predictions for trial duration, patient dropout rate, serious adverse event, mortality rate, trial approval outcome, trial failure reason, drug dose finding, and design of eligibility criteria. Additionally, the authors provide basic validation methods for each task to ensure the datasets’ usability and reliability. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Clinical trials are crucial for developing new medical treatments, but they can be risky and time-consuming. To help guide these trials, researchers are using artificial intelligence (AI) to predict important events. However, collecting and organizing the right data has been a challenge. This paper solves this problem by creating a set of AI-ready datasets that include various types of information and predictions for different challenges in clinical trial design. |
Keywords
* Artificial intelligence * Dropout * Multi modal
