Summary of Ehr-seqsql : a Sequential Text-to-sql Dataset For Interactively Exploring Electronic Health Records, by Jaehee Ryu et al.
EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
by Jaehee Ryu, Seonhee Cho, Gyubok Lee, Edward Choi
First submitted to arxiv on: 23 May 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB); Information Retrieval (cs.IR)
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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 introduces EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases. The dataset is designed to address critical aspects in text-to-SQL parsing, including interactivity, compositionality, and efficiency. EHR-SeqSQL is the largest and first medical text-to-SQL dataset benchmark that includes sequential and contextual questions. The paper provides a data split and a new test set to assess compositional generalization ability. Experiments demonstrate the superiority of a multi-turn approach over a single-turn approach in learning compositionality. Additionally, the dataset integrates specially crafted tokens into SQL queries to improve execution efficiency. With EHR-SeqSQL, the authors aim to bridge the gap between practical needs and academic research in the text-to-SQL domain. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary EHR-SeqSQL is a new kind of database that helps doctors and researchers work with medical records more easily. It’s like a super-smart translator that can understand what you’re asking about medical records and give you the right information quickly. This paper shows that using EHR-SeqSQL makes it easier for computers to learn how to ask questions about medical records in a way that’s helpful for doctors and researchers. |
Keywords
» Artificial intelligence » Generalization » Parsing
