ArticleAMIA ... Annual Symposium proceedings. AMIA Symposium2017
A Semantic Parsing Method for Mapping Clinical Questions to Logical Forms.
Article in AMIA ... Annual Symposium proceedings. AMIA Symposium, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- quEHRy: a question answering system to query electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Toward a Neural Semantic Parsing System for EHR Question Answering.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2022Article
- A BERT-Based Generation Model to Transform Medical Texts to SQL Queries for Electronic Medical Records: Model Development and Validation.JMIR medical informatics · 2021Article
- Paraphrasing to improve the performance of Electronic Health Records Question Answering.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2020Article
- Using FHIR to Construct a Corpus of Clinical Questions Annotated with Logical Forms and Answers.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This paper presents a method for converting natural language questions about structured data in the electronic health record (EHR) into logical forms. The logical forms can then subsequently be converted to EHR-dependent structured queries. The natural language processing task, known as semantic parsing, has the potential to convert questions to logical forms with extremely high precision, resulting in a system that is usable and trusted by clinicians for real-time use in clinical settings. We propose a hybrid semantic parsing method, combining rule-based methods with a machine learning-based classifier. The overall semantic parsing precision on a set of 212 questions is 95.6%. The parser's rules furthermore allow it to "know what it does not know", enabling the system to indicate when unknown terms prevent it from understanding the question's full logical structure. When combined with a module for converting a logical form into an EHR-dependent query, this high-precision approach allows for a question answering system to provide a user with a single, verifiably correct answer.
Indexed as
Identifiers
29854217PMC5977685What 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.