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Towards Understanding the Generalization of Medical Text-to-SQL Models and Datasets.

Richard Tarbell1, Kim-Kwang Raymond Choo2, Glenn Dietrich2

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Generating accurate text-to-SQL for electronic medical records (EMRs) remains challenging. New data splits reveal significant performance drops for current models, highlighting the need for improved generalizability in medical data analysis.

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Database Management

Background:

  • Electronic medical records (EMRs) are complex relational databases.
  • Accessing EMR data requires specialized database knowledge, hindering research.
  • Text-to-SQL aims to simplify data retrieval for healthcare professionals.

Purpose of the Study:

  • To reassess the true performance of text-to-SQL models in the medical domain.
  • To highlight the limitations of existing datasets in evaluating model generalizability.
  • To introduce methods for improving text-to-SQL model robustness for EMR data.

Main Methods:

  • Created new, more challenging data splits of the MIMICSQL dataset.
  • Evaluated state-of-the-art language models on these new splits.
  • Developed and applied a novel data augmentation technique.

Main Results:

  • State-of-the-art models showed accuracy drops from over 90% to as low as 28% on new data splits.
  • Existing datasets do not adequately represent the difficulty of real-world medical text-to-SQL tasks.
  • Data augmentation improved model generalizability.

Conclusions:

  • Current text-to-SQL models are not sufficiently generalizable for complex medical EMR data.
  • Further research is needed to develop robust text-to-SQL solutions for healthcare.
  • This work provides a foundation for more reliable medical data access via natural language.