Summary of Trialenroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models, by Ling Yue et al.
TrialEnroll: Predicting Clinical Trial Enrollment Success with Deep & Cross Network and Large Language Models
by Ling Yue, Sixue Xing, Jintai Chen, Tianfan Fu
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 proposes a novel deep and cross network model augmented with large language models to predict the success of clinical trial recruitment. The approach learns semantic information from trial eligibility criteria and provides interpretability by highlighting which specific sentences or words contribute most to the prediction. The method outperforms established machine learning methods, achieving an empirical PR-AUC score of 0.7002. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps predict whether a clinical trial will be successful in recruiting enough patients before it even starts. This is important because it saves time and resources. The researchers created a new type of AI model that looks at the words used to describe who can participate in the trial and uses this information to make a prediction. They also made sure their method can explain why certain words or sentences are important for making the prediction. |
Keywords
» Artificial intelligence » Auc » Machine learning
