  Question Answering (QA) is a widely-used framework for developing and
evaluating an intelligent machine. In this light, QA on Electronic Health
Records (EHR), namely EHR QA, can work as a crucial milestone towards
developing an intelligent agent in healthcare. EHR data are typically stored in
a relational database, which can also be converted to a directed acyclic graph,
allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based
QA. We hypothesize that the graph-based approach is more suitable for EHR QA as
graphs can represent relations between entities and values more naturally
compared to tables, which essentially require JOIN operations. In this paper,
we propose a graph-based EHR QA where natural language queries are converted to
SPARQL instead of SQL. To validate our hypothesis, we create four EHR QA
datasets (graph-based VS table-based, and simplified database schema VS
original database schema), based on a table-based dataset MIMICSQL. We test
both a simple Seq2Seq model and a state-of-the-art EHR QA model on all datasets
where the graph-based datasets facilitated up to 34% higher accuracy than the
table-based dataset without any modification to the model architectures.
Finally, all datasets are open-sourced to encourage further EHR QA research in
both directions.

Databases;Artificial Intelligence;Machine Learning