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

Youcheng Pan1, Chenghao Wang1, Baotian Hu1

  • 1Intelligent Computing Research Center, Harbin Institute of Technology, Shenzhen, China.

JMIR Medical Informatics
|December 10, 2021
PubMed
Summary
This summary is machine-generated.

A new model, MedTS, translates medical text into SQL queries for electronic medical records (EMRs). This advanced text-to-SQL system significantly improves data retrieval accuracy in healthcare.

Keywords:
BERTelectronic medical recordgrammar-based decodingtext-to-SQL generationtree-structured intermediate representation

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Database Management

Background:

  • Electronic medical records (EMRs) are stored in relational databases, necessitating SQL queries for data retrieval.
  • Medical experts face challenges in formulating SQL queries due to specialized knowledge requirements.
  • Current text-to-SQL generation methods have limited adoption within the medical domain.

Purpose of the Study:

  • To develop a neural generation model for automatic transformation of medical text into SQL queries for EMRs.
  • To create a model that integrates medical text characteristics with SQL structure for improved query generation.

Main Methods:

  • Proposed MedTS (Medical Text-to-SQL) model using a pretrained Bidirectional Encoder Representations From Transformers (BERT) encoder.
  • Employed a grammar-based long short-term memory network decoder to predict an intermediate syntax tree representation.
  • Utilized syntax trees as intermediate representations to align with SQL's structure and reduce generation search space.

Main Results:

  • MedTS achieved 0.784 accuracy in logic form and 0.899 in execution on the MIMICSQL dataset.
  • The model significantly outperformed existing state-of-the-art text-to-SQL methods.
  • Performance across generated SQL components was balanced and showed substantial improvements.

Conclusions:

  • The MedTS model demonstrates effectiveness and robustness in medical text-to-SQL generation.
  • The system shows strong potential for practical application in real-world medical scenarios.
  • This advancement can enhance data accessibility and utilization within EMR systems.