Recently, deep learning-based language models have significantly enhanced
text-to-SQL tasks, with promising applications in retrieving patient records
within the medical domain. One notable challenge in such applications is
discerning unanswerable queries. Through fine-tuning model, we demonstrate the
feasibility of converting medical record inquiries into SQL queries.
Additionally, we introduce an entropy-based method to identify and filter out
unanswerable results. We further enhance result quality by filtering
low-confidence SQL through log probability-based distribution, while
grammatical and schema errors are mitigated by executing queries on the actual
database. We experimentally verified that our method can filter unanswerable
questions, which can be widely utilized even when the parameters of the model
are not accessible, and that it can be effectively utilized in practice.

利用深度学习的语言模型对医学领域的病历查询进行转换成 SQL 查询的实证研究，并使用熵基方法和基于概率分布的方法过滤无法回答的结果，从而实现了有效的病历查询结果过滤。