  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.
