mlsanityData to 14 Oct 2026

Uncertainty-Aware Text-to-Program for Question Answering on Structured Electronic Health Records

arXiv 2203.06918 ↗First posted 14 Mar 2022cs.CL cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 19 of its 30 references that are in the map. First posted on arXiv 14 Mar 2022.

Builds on

19 of 30 references.

References · 19

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7 more
  1. 2018Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graphcited by 67
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Cited by year

Citing papers per year: 2023: 2; 2024: 1; 2025: 1.

4 papers.

  1. 2024TrustSQL: Benchmarking Text-to-SQL Reliability with Penalty-Based Scoringcited by 17
  2. 2023A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promisescited by 13
  3. 2023Question Answering for Electronic Health Records: A Scoping Review of datasets and modelscited by 7
  4. 2025Comprehensive Modeling and Question Answering of Cancer Clinical Practice Guidelines using LLMscited by 2