  Question Answering on Electronic Health Records (EHR-QA) has a significant
impact on the healthcare domain, and it is being actively studied. Previous
research on structured EHR-QA focuses on converting natural language queries
into query language such as SQL or SPARQL (NLQ2Query), so the problem scope is
limited to pre-defined data types by the specific query language. In order to
expand the EHR-QA task beyond this limitation to handle multi-modal medical
data and solve complex inference in the future, more primitive systemic
language is needed. In this paper, we design the program-based model
(NLQ2Program) for EHR-QA as the first step towards the future direction. We
tackle MIMICSPARQL*, the graph-based EHR-QA dataset, via a program-based
approach in a semi-supervised manner in order to overcome the absence of gold
programs. Without the gold program, our proposed model shows comparable
performance to the previous state-of-the-art model, which is an NLQ2Query model
(0.9% gain). In addition, for a reliable EHR-QA model, we apply the uncertainty
decomposition method to measure the ambiguity in the input question. We
empirically confirmed data uncertainty is most indicative of the ambiguity in
the input question.
