  Text-to-SQL models are pivotal for making Electronic Health Records (EHRs)
accessible to healthcare professionals without SQL knowledge. With the
advancements in large language models, these systems have become more adept at
translating complex questions into SQL queries. Nonetheless, the critical need
for reliability in healthcare necessitates these models to accurately identify
unanswerable questions or uncertain predictions, preventing misinformation. To
address this problem, we present a self-training strategy using pseudo-labeled
unanswerable questions to enhance the reliability of text-to-SQL models for
EHRs. This approach includes a two-stage training process followed by a
filtering method based on the token entropy and query execution. Our
methodology's effectiveness is validated by our top performance in the EHRSQL
2024 shared task, showcasing the potential to improve healthcare
decision-making through more reliable text-to-SQL systems.
