Summary of Exploring the Generalization Of Cancer Clinical Trial Eligibility Classifiers Across Diseases, by Yumeng Yang et al.
Exploring the Generalization of Cancer Clinical Trial Eligibility Classifiers Across Diseases
by Yumeng Yang, Ashley Gilliam, Ethan B Ludmir, Kirk Roberts
First submitted to arxiv on: 25 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG); Quantitative Methods (q-bio.QM)
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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 The paper investigates the effectiveness of machine learning models in classifying clinical trial eligibility criteria across various diseases and phases. By analyzing a comprehensive dataset comprising 2,490 annotated eligibility criteria from five types of trials, including cancer, heart disease, type 2 diabetes, and observational studies, researchers found that models trained on extensive cancer datasets can accurately handle criteria commonly found in non-cancer trials. However, they struggle with criteria specific to cancer trials, such as prior malignancy. The study also explores the potential of few-shot learning, demonstrating that a limited number of disease-specific examples can partially bridge this performance gap. By releasing this new dataset, the researchers aim to promote cross-disease generalization in clinical trial classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how artificial intelligence (AI) can help make clinical trials more successful by better understanding who can join them. The study focuses on checking if AI models trained on one type of trial can be used for other types of trials, like cancer or heart disease trials. Researchers collected data from five different types of trials and found that AI models are good at recognizing criteria commonly found in non-cancer trials, but struggle with criteria specific to cancer trials. The study also shows that even a small number of examples from each type of trial can help the AI models do better. By sharing this new dataset, the researchers hope to help other scientists develop AI systems that can be used across different types of clinical trials. |
Keywords
* Artificial intelligence * Classification * Few shot * Generalization * Machine learning
