ArticleAMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science2020
Paraphrasing to improve the performance of Electronic Health Records Question Answering.
Article in AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- privateST: a feasible framework for privacy-preserving spatial transcriptomics prediction from histopathology images.Scientific reports · 2026Article
- Benchmarking the Confidence of Large Language Models in Answering Clinical Questions: Cross-Sectional Evaluation Study.JMIR medical informatics · 2025Article
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper describes a paraphrasing approach to improve the performance of question answering (QA) for electronic health records (EHRs). QA systems for structured EHR data usually rely on semantic parsing, which aims to generate machine-understandable logical forms from free-text questions. Training semantic parsers requires large datasets of question-logical form (QL) pairs, which are labor-intensive to create. Considering the scarcity of large QL datasets in the clinical domain, we propose a framework for expanding an existing dataset using paraphrasing. We experiment with different heuristics for multiple sample sizes and iterations to assess the effect of adding paraphrasing to the task of semantic parsing. We found that adding paraphrases to an existing dataset based on TERTHRESHOLD scores results in an improved performance in the majority (74%) of the experimental runs. Hence, the proposed paraphrasing-based framework has the potential to improve the performance of QA systems using a limited set of existing QL annotations.
Identifiers
32477685PMC7233085What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.