Summary of Overview Of the Ehrsql 2024 Shared Task on Reliable Text-to-sql Modeling on Electronic Health Records, by Gyubok Lee et al.
Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling on Electronic Health Records
by Gyubok Lee, Sunjun Kweon, Seongsu Bae, Edward Choi
First submitted to arxiv on: 4 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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 proposed EHRSQL 2024 shared task aims to develop a question-answering system for Electronic Health Records (EHRs) using text-to-SQL modeling. The goal is to create a reliable system that can provide requested answers to healthcare professionals, improving their clinical work processes and satisfying their needs. Eight teams participated in the task, employing various methods to solve it. This paper describes the task, dataset, methods, and results of the participants. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The EHRSQL 2024 shared task is an effort to make information retrieval from Electronic Health Records (EHRs) more accessible by developing a question-answering system using text-to-SQL modeling. Healthcare professionals can ask natural language questions, which are translated into SQL queries and used to retrieve answers. This system aims to improve clinical work processes and satisfy healthcare professionals’ needs. |
Keywords
» Artificial intelligence » Question answering
