JMIR Publications

ABSTRACT

Background:

Electronic medical records (EMRs) are usually stored in relational databases that require structured query language (SQL) queries to retrieve information of interest. Effectively completing such queries is usually a challenging task for medical experts due to the barriers in expertise. However, 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, which can jointly consider the characteristics of medical text and the SQL structure, to automatically transform medical texts to SQL queries for EMRs.

Methods:

In contrast to regarding the SQL query as an ordinary word sequence, the syntax tree, introduced as an intermediate representation, is more in line with the tree-structure nature of SQL and also can effectively reduce the search space during generation. We proposed a medical text-to-SQL model (MedTS), which employed a pre-trained BERT as the encoder and leveraged a grammar-based LSTM as the decoder to predict the tree-structured intermediate representation that can be easily transformed to the final SQL query. Experiments are conducted on the MIMICSQL dataset and five competitor methods are compared.

Results:

Experimental results demonstrated that MedTS achieved the accuracy of 0.770 and 0.888 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 has substantial improvements.

Conclusions:

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