Summary of Ctbench: a Comprehensive Benchmark For Evaluating Language Model Capabilities in Clinical Trial Design, by Nafis Neehal et al.
CTBench: A Comprehensive Benchmark for Evaluating Language Model Capabilities in Clinical Trial Design
by Nafis Neehal, Bowen Wang, Shayom Debopadhaya, Soham Dan, Keerthiram Murugesan, Vibha Anand, Kristin P. Bennett
First submitted to arxiv on: 25 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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 CTBench is a novel benchmark designed to assess language models (LMs) in supporting clinical study design. The benchmark evaluates LMs’ ability to identify baseline features of clinical trials, including demographic and relevant data collected at the trial’s inception from all participants. These features are crucial for characterizing study cohorts and validating results. CTBench consists of two datasets: “CT-Repo” containing 1,690 clinical trials sourced from ClinicalTrials.gov, and “CT-Pub,” a subset of 100 trials with more comprehensive baseline features gathered from relevant publications. Two LM-based evaluation methods, “ListMatch-LM” and “ListMatch-BERT,” are developed to compare actual baseline feature lists against LM-generated responses using GPT-4o and BERT scores. The performance of GPT-4o as an evaluator is validated through human-in-the-loop evaluations on the CT-Pub dataset, where clinical experts confirm matches between actual and LM-generated features. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new tool called CTBench that helps scientists design better medical studies. It’s like a test to see how good language models are at finding important details about people who participate in these studies. The tool looks at two types of data: lots of information from past studies and specific features from recent publications. It uses special techniques to compare what the language models think are important features with what real experts think are important. This helps scientists see if language models can really help them design better medical studies. |
Keywords
» Artificial intelligence » Bert » Gpt
