Toward a Neural Semantic Parsing System for EHR Question Answering
Toward a Neural Semantic Parsing System for EHR Question Answering
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DOI：
10.48550/arxiv.2211.04569
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
Sarvesh Soni;Kirk Roberts
中科院分区：
文献类型：
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作者：
Sarvesh Soni;Kirk Roberts
临床语义解析 (SP) 是从旨在从电子健康记录 (EHR) 中检索信息的自然语言查询中识别确切信息需求（作为机器可理解的逻辑形式）的重要一步。当前的临床 SP 方法主要基于传统的机器学习，并且需要手动构建词典。神经 SP 的最新进展表明，无需太多人力即可构建强大且灵活的语义解析器。因此，在本文中，我们的目标是系统评估两种此类神经 SP 模型在 EHR 问答 (QA) 中的性能。我们发现这些先进的神经模型在两个临床 SP 数据集上的性能是有希望的，因为它们易于应用且具有普遍性。我们的错误分析揭示了这些模型所犯的常见错误类型，并有可能为未来提高 EHR QA 神经 SP 模型性能的研究提供信息。
Clinical semantic parsing (SP) is an important step toward identifying the exact information need (as a machine-understandable logical form) from a natural language query aimed at retrieving information from electronic health records (EHRs). Current approaches to clinical SP are largely based on traditional machine learning and require hand-building a lexicon. The recent advancements in neural SP show a promise for building a robust and flexible semantic parser without much human effort. Thus, in this paper, we aim to systematically assess the performance of two such neural SP models for EHR question answering (QA). We found that the performance of these advanced neural models on two clinical SP datasets is promising given their ease of application and generalizability. Our error analysis surfaces the common types of errors made by these models and has the potential to inform future research into improving the performance of neural SP models for EHR QA.