- 참고
- [1] Lee, Gyubok, et al. "Ehrsql: A practical text-to-sql benchmark for electronic health records." Advances in Neural Information Processing Systems 35 (2022): 15589-15601.
[2] Choi, Edward, et al. “Reliable Text-to-SQL Modeling on Electronic Health Records - NAACL Clinical NLP 2024 Shared Task” https://sites.google.com/view/ehrsql-2024
[3] “Evaluation Tab of Reliable Text-to-SQL Modeling on Electronic Health Records” https://www.codabench.org/competitions/1889/
[4] “The 6th Clinical Natural Language Processing Workshop” https://clinical-nlp.github.io/2024/
[5] Jo, Yongrae, et al. "LG AI Research & KAIST at EHRSQL 2024: Self-Training Large Language Models with Pseudo-Labeled Unanswerable Questions for a Reliable Text-to-SQL System on EHRs" arXiv preprint arXiv:2405.11162 (2024)
