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.

本文设计了程序为基础的模型（NLQ2Program）以利于未来拓展医疗多模态数据和解决复杂推理的电子健康记录问答任务，该模型在没有金标程序的情况下表现良好，运用不确定性分解方法可测度输入问题的模糊性。