ArticleLREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation2016
Annotating Logical Forms for EHR Questions.
Article in LREC ... International Conference on Language Resources & Evaluation : [proceedings]. International Conference on Language Resources & Evaluation, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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The trial behind it
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Who cites it
9 citing papers in PubMed.
- Question Answering for Electronic Health Records: Scoping Review of Datasets and Models.Journal of medical Internet research · 2024Article
- LeafAI: query generator for clinical cohort discovery rivaling a human programmer.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- 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
- Different Data Mining Approaches Based Medical Text Data.Journal of healthcare engineering · 2021Review
- 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
- Semantic annotation of consumer health questions.BMC bioinformatics · 2018Article
- A Semantic Parsing Method for Mapping Clinical Questions to Logical Forms.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
This paper discusses the creation of a semantically annotated corpus of questions about patient data in electronic health records (EHRs). The goal is to provide the training data necessary for semantic parsers to automatically convert EHR questions into a structured query. A layered annotation strategy is used which mirrors a typical natural language processing (NLP) pipeline. First, questions are syntactically analyzed to identify multi-part questions. Second, medical concepts are recognized and normalized to a clinical ontology. Finally, logical forms are created using a lambda calculus representation. We use a corpus of 446 questions asking for patient-specific information. From these, 468 specific questions are found containing 259 unique medical concepts and requiring 53 unique predicates to represent the logical forms. We further present detailed characteristics of the corpus, including inter-annotator agreement results, and describe the challenges automatic NLP systems will face on this task.
Indexed as
Identifiers
28503677PMC5428549What 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.