Transforming natural language questions into SQL queries is crucial for
precise data retrieval from electronic health record (EHR) databases. A
significant challenge in this process is detecting and rejecting unanswerable
questions that request information beyond the database's scope or exceed the
system's capabilities. In this paper, we introduce a novel text-to-SQL
framework that robustly handles out-of-domain questions and verifies the
generated queries with query execution.Our framework begins by standardizing
the structure of questions into a templated format. We use a powerful large
language model (LLM), fine-tuned GPT-3.5 with detailed prompts involving the
table schemas of the EHR database system. Our experimental results demonstrate
the effectiveness of our framework on the EHRSQL-2024 benchmark benchmark, a
shared task in the ClinicalNLP workshop. Although a straightforward fine-tuning
of GPT shows promising results on the development set, it struggled with the
out-of-domain questions in the test set. With our framework, we improve our
system's adaptability and achieve competitive performances in the official
leaderboard of the EHRSQL-2024 challenge.

使用新颖的文本至 SQL 框架，我们处理超出数据库范围或超出系统能力的无法回答的问题，将其标准化并与表模式相结合，以确保准确的数据检索，实现在临床自然语言处理工作坊的 EHRSQL-2024 挑战中具有竞争力的表现。