Electronic medical records (EMR) contain comprehensive patient information
and are typically stored in a relational database with multiple tables.
Effective and efficient patient information retrieval from EMR data is a
challenging task for medical experts. Question-to-SQL generation methods tackle
this problem by first predicting the SQL query for a given question about a
database, and then, executing the query on the database. However, most of the
existing approaches have not been adapted to the healthcare domain due to a
lack of healthcare Question-to-SQL dataset for learning models specific to this
domain. In addition, wide use of the abbreviation of terminologies and possible
typos in questions introduce additional challenges for accurately generating
the corresponding SQL queries. In this paper, we tackle these challenges by
developing a deep learning based TRanslate-Edit Model for Question-to-SQL
(TREQS) generation, which adapts the widely used sequence-to-sequence model to
directly generate the SQL query for a given question, and further performs the
required edits using an attentive-copying mechanism and task-specific look-up
tables. Based on the widely used publicly available electronic medical
database, we create a new large-scale Question-SQL pair dataset, named
MIMICSQL, in order to perform the Question-to-SQL generation task in healthcare
domain. An extensive set of experiments are conducted to evaluate the
performance of our proposed model on MIMICSQL. Both quantitative and
qualitative experimental results indicate the flexibility and efficiency of our
proposed method in predicting condition values and its robustness to random
questions with abbreviations and typos.

本文利用深度学习技术提出了一种新的基于 Translate-Edit 模型的健康医疗 Question-to-SQL 语句生成方法，并在公开的电子病历数据库 MIMIC 上进行了广泛实验。结果表明，该方法可适应医疗领域中常见的缩写与错别字，对病情信息的预测表现良好。