Summary of Acr: a Benchmark For Automatic Cohort Retrieval, by Dung Ngoc Thai et al.
ACR: A Benchmark for Automatic Cohort Retrieval
by Dung Ngoc Thai, Victor Ardulov, Jose Ulises Mena, Simran Tiwari, Gleb Erofeev, Ramy Eskander, Karim Tarabishy, Ravi B Parikh, Wael Salloum
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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 new approach to identifying patient cohorts in healthcare organizations, which is crucial for tasks such as clinical trial recruitment and retrospective studies. Current methods rely on automated queries of structured data combined with manual curation, but are time-consuming, labor-intensive, and often yield low-quality results. The authors introduce the task of Automatic Cohort Retrieval (ACR) and evaluate the performance of large language models (LLMs) and commercial neuro-symbolic approaches. They provide a benchmark task, query dataset, EMR dataset, and evaluation framework. The findings highlight the need for efficient, high-quality ACR systems that can reason longitudinally across extensive patient databases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding groups of patients with similar characteristics in hospitals and clinics. Right now, doctors and researchers have to search through lots of computer records to find these groups, which takes a lot of time and effort. The authors think they can make this process better by using special kinds of computers called large language models (LLMs) and information retrieval systems. They want to create a new way to find patient groups that is fast, accurate, and works well with the complex medical records used today. |
