EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records

• URL: http://arxiv.org/abs/2406.00019v3
• Date: Tue, 30 Jul 2024 10:09:13 GMT
• Title: EHR-SeqSQL : A Sequential Text-to-SQL Dataset For Interactively Exploring Electronic Health Records
• Authors: Jaehee Ryu, Seonhee Cho, Gyubok Lee, Edward Choi,
• Abstract summary: We introduce EHR-Seq, a novel sequential text-to-sql dataset for EHR databases. EHR-Seq is the first medical text-to-sql dataset benchmark to include sequential and contextual questions. Our experiments demonstrate the superiority of a multi-turn approach over a single-turn approach in compositionality.
• Score: 11.78795632771211
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we introduce EHR-SeqSQL, a novel sequential text-to-SQL dataset for Electronic Health Record (EHR) databases. EHR-SeqSQL is designed to address critical yet underexplored aspects in text-to-SQL parsing: interactivity, compositionality, and efficiency. To the best of our knowledge, EHR-SeqSQL is not only the largest but also the first medical text-to-SQL dataset benchmark to include sequential and contextual questions. We provide a data split and the new test set designed to assess compositional generalization ability. Our experiments demonstrate the superiority of a multi-turn approach over a single-turn approach in learning compositionality. Additionally, our dataset integrates specially crafted tokens into SQL queries to improve execution efficiency. With EHR-SeqSQL, we aim to bridge the gap between practical needs and academic research in the text-to-SQL domain. EHR-SeqSQL is available at https://github.com/seonhee99/EHR-SeqSQL.

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