ArticleBMC bioinformatics2018
Semantic annotation of consumer health questions.
Article in BMC bioinformatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Article
- Semantic classification of Indonesian consumer health questions.Journal of biomedical semantics · 2025Article
- LLM enabled classification of patient self-reported symptoms and needs in health systems across the USA.NPJ digital medicine · 2025Article
- A Dataset of Medical Questions Paired with Automatically Generated Answers and Evidence-supported References.Scientific data · 2025Article
- CHQ- SocioEmo: Identifying Social and Emotional Support Needs in Consumer-Health Questions.Scientific data · 2023Article
- Classifying unstructured electronic consult messages to understand primary care physician specialty information needs.Journal of the American Medical Informatics Association : JAMIA · 2022Article
- Question-aware transformer models for consumer health question summarization.Journal of biomedical informatics · 2022Article
- Dataset search in biodiversity research: Do metadata in data repositories reflect scholarly information needs?PloS one · 2021Article
- An automated method to enrich consumer health vocabularies using GloVe word embeddings and an auxiliary lexical resource.PeerJ. Computer science · 2021Article
- Multidimensional Feature Classification of the Health Information Needs of Patients With Hypertension in an Online Health Community Through Analysis of 1000 Patient Question Records: Observational Study.Journal of medical Internet research · 2020Observational
- Patient Questions and Physician Responses in a Chinese Health Q&A Website: Content Analysis.Journal of medical Internet research · 2020Article
- Consumer health information and question answering: helping consumers find answers to their health-related information needs.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- A question-entailment approach to question answering.BMC bioinformatics · 2019Article
- On the Role of Question Summarization and Information Source Restriction in Consumer Health Question Answering.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2019Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
backgroundConsumers increasingly use online resources for their health information needs. While current search engines can address these needs to some extent, they generally do not take into account that most health information needs are complex and can only fully be expressed in natural language. Consumer health question answering (QA) systems aim to fill this gap. A major challenge in developing consumer health QA systems is extracting relevant semantic content from the natural language questions (question understanding). To develop effective question understanding tools, question corpora semantically annotated for relevant question elements are needed. In this paper, we present a two-part consumer health question corpus annotated with several semantic categories: named entities, question triggers/types, question frames, and question topic. The first part (CHQA-email) consists of relatively long email requests received by the U.S. National Library of Medicine (NLM) customer service, while the second part (CHQA-web) consists of shorter questions posed to MedlinePlus search engine as queries. Each question has been annotated by two annotators. The annotation methodology is largely the same between the two parts of the corpus; however, we also explain and justify the differences between them. Additionally, we provide information about corpus characteristics, inter-annotator agreement, and our attempts to measure annotation confidence in the absence of adjudication of annotations.
resultsThe resulting corpus consists of 2614 questions (CHQA-email: 1740, CHQA-web: 874). Problems are the most frequent named entities, while treatment and general information questions are the most common question types. Inter-annotator agreement was generally modest: question types and topics yielded highest agreement, while the agreement for more complex frame annotations was lower. Agreement in CHQA-web was consistently higher than that in CHQA-email. Pairwise inter-annotator agreement proved most useful in estimating annotation confidence.
conclusionsTo our knowledge, our corpus is the first focusing on annotation of uncurated consumer health questions. It is currently used to develop machine learning-based methods for question understanding. We make the corpus publicly available to stimulate further research on consumer health QA.
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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.