Electronic Health Records (EHRs) are relational databases that store the
entire medical histories of patients within hospitals. They record numerous
aspects of patients' medical care, from hospital admission and diagnosis to
treatment and discharge. While EHRs are vital sources of clinical data,
exploring them beyond a predefined set of queries requires skills in query
languages like SQL. To make information retrieval more accessible, one strategy
is to build a question-answering system, possibly leveraging text-to-SQL models
that can automatically translate natural language questions into corresponding
SQL queries and use these queries to retrieve the answers. The EHRSQL 2024
shared task aims to advance and promote research in developing a
question-answering system for EHRs using text-to-SQL modeling, capable of
reliably providing requested answers to various healthcare professionals to
improve their clinical work processes and satisfy their needs. Among more than
100 participants who applied to the shared task, eight teams completed the
entire shared task processes and demonstrated a wide range of methods to
effectively solve this task. In this paper, we describe the task of reliable
text-to-SQL modeling, the dataset, and the methods and results of the
participants. We hope this shared task will spur further research and insights
into developing reliable question-answering systems for EHRs.

电子健康档案（EHRs）是存储医院患者整个病史的关系型数据库，通过构建问答系统、利用文本到 SQL 的模型将自然语言问题转化为对应的 SQL 查询并使用这些查询检索答案，可以提供医疗专业人员所需的答案，改进其临床工作流程并满足其需求。

EHRSQL 2024 可靠电子健康记录文本到 SQL 建模共享任务概述

Overview of the EHRSQL 2024 Shared Task on Reliable Text-to-SQL Modeling  on Electronic Health Records

Translating natural language utterances to executable queries is a helpful
technique in making the vast amount of data stored in relational databases
accessible to a wider range of non-tech-savvy end users. Prior work in this
area has largely focused on textual input that is linguistically correct and
semantically unambiguous. However, real-world user queries are often succinct,
colloquial, and noisy, resembling the input of a search engine. In this work,
we introduce data augmentation techniques and a sampling-based content-aware
BERT model (ColloQL) to achieve robust text-to-SQL modeling over natural
language search (NLS) questions. Due to the lack of evaluation data, we curate
a new dataset of NLS questions and demonstrate the efficacy of our approach.
ColloQL's superior performance extends to well-formed text, achieving 84.9%
(logical) and 90.7% (execution) accuracy on the WikiSQL dataset, making it, to
the best of our knowledge, the highest performing model that does not use
execution guided decoding.

使用数据增强技术和基于采样的内容感知 BERT 模型 (ColloQL) 实现了鲁棒的自然语言搜索 (NLS) 查询的文本到 SQL 建模，评估表明该方法优于现有技术。