A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation

Youcheng Pan; Chenghao Wang; Baotian Hu; Yang Xiang; Xiaolong Wang; Qingcai Chen; Junjie Chen; Jingcheng Du

doi:10.2196/32698

A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation

JMIR Med Inform. 2021 Dec 8;9(12):e32698. doi: 10.2196/32698.

Authors

Youcheng Pan¹, Chenghao Wang¹, Baotian Hu¹, Yang Xiang², Xiaolong Wang¹, Qingcai Chen^{1

2}, Junjie Chen¹, Jingcheng Du³

Affiliations

¹ Intelligent Computing Research Center, Harbin Institute of Technology, Shenzhen, China.
² Peng Cheng Laboratory, Shenzhen, China.
³ University of Texas Health Science Center at Houston, Houston, TX, United States.

PMID: 34889749
PMCID: PMC8701710
DOI: 10.2196/32698

Abstract

Background: Electronic medical records (EMRs) are usually stored in relational databases that require SQL queries to retrieve information of interest. Effectively completing such queries can be a challenging task for medical experts due to the barriers in expertise. Existing text-to-SQL generation studies have not been fully embraced in the medical domain.

Objective: The objective of this study was to propose a neural generation model that can jointly consider the characteristics of medical text and the SQL structure to automatically transform medical texts to SQL queries for EMRs.

Methods: We proposed a medical text-to-SQL model (MedTS), which employed a pretrained Bidirectional Encoder Representations From Transformers model as the encoder and leveraged a grammar-based long short-term memory network as the decoder to predict the intermediate representation that can easily be transformed into the final SQL query. We adopted the syntax tree as the intermediate representation rather than directly regarding the SQL query as an ordinary word sequence, which is more in line with the tree-structure nature of SQL and can also effectively reduce the search space during generation. Experiments were conducted on the MIMICSQL dataset, and 5 competitor methods were compared.

Results: Experimental results demonstrated that MedTS achieved the accuracy of 0.784 and 0.899 on the test set in terms of logic form and execution, respectively, which significantly outperformed the existing state-of-the-art methods. Further analyses proved that the performance on each component of the generated SQL was relatively balanced and offered substantial improvements.

Conclusions: The proposed MedTS was effective and robust for improving the performance of medical text-to-SQL generation, indicating strong potential to be applied in the real medical scenario.

Keywords: BERT; electronic medical record; grammar-based decoding; text-to-SQL generation; tree-structured intermediate representation.

©Youcheng Pan, Chenghao Wang, Baotian Hu, Yang Xiang, Xiaolong Wang, Qingcai Chen, Junjie Chen, Jingcheng Du. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 08.12.2021.