Interactive use of online health resources: a comparison of consumer and professional questions

Roberts, Kirk; Demner‐Fushman, Dina

doi:10.1093/jamia/ocw024

Cited by 39 publications

(26 citation statements)

References 22 publications

(26 reference statements)

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“…Answers allow consumers to ask questions in full sentences using natural language. Such questions also tend to contain more context information, and more closely resemble open-domain language than texts written by professionals [32]. Meanwhile, the answers provided on the site are also mainly written in lay language.…”

Section: Methodsmentioning

confidence: 99%

Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach

Chen

et al. 2017

Journal of Biomedical Informatics

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The widely known vocabulary gap between health consumers and healthcare professionals hinders information seeking and health dialogue of consumers on end-user health applications. The Open Access and Collaborative Consumer Health Vocabulary (OAC CHV), which contains health-related terms used by lay consumers, has been created to bridge such a gap. Specifically, the OAC CHV facilitates consumers’ health information retrieval by enabling consumer-facing health applications to translate between professional language and consumer friendly language. To keep up with the constantly evolving medical knowledge and language use, new terms need to be identified and added to the OAC CHV. User-generated content on social media, including social question and answer (social Q&A) sites, afford us an enormous opportunity in mining consumer health terms. Existing methods of identifying new consumer terms from text typically use ad-hoc lexical syntactic patterns and human review. Our study extends an existing method by extracting n-grams from a social Q&A textual corpus and representing them with a rich set of contextual and syntactic features. Using K-means clustering, our method, simiTerm, was able to identify terms that are both contextually and syntactically similar to the existing OAC CHV terms. We tested our method on social Q&A corpora on two disease domains: diabetes and cancer. Our method outperformed three baseline ranking methods. A post-hoc qualitative evaluation by human experts further validated that our method can effectively identify meaningful new consumer terms on social Q&A.

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Section: Methodsmentioning

confidence: 99%

Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach

Chen

et al. 2017

Journal of Biomedical Informatics

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“…Classification schema is the basis of question classification. However, due to the difference in the information needs of health providers and consumers [9-11], the existing question classification schemas for professional health-related questions (the International Classification of Primary Care [12,13], the Taxonomies of Generic Clinical Questions (TGCQ) [14], etc) are not suitable for consumer health questions. Although some research focuses on the classification schema of consumer health questions [15,16], it has not been defined in a systematic manner yet.…”

Section: Introductionmentioning

confidence: 99%

Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet

Guo¹,

Xu²,

Hou³

et al. 2017

J Med Internet Res

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BackgroundIn question answering (QA) system development, question classification is crucial for identifying information needs and improving the accuracy of returned answers. Although the questions are domain-specific, they are asked by non-professionals, making the question classification task more challenging.ObjectiveThis study aimed to classify health care–related questions posted by the general public (Chinese speakers) on the Internet.MethodsA topic-based classification schema for health-related questions was built by manually annotating randomly selected questions. The Kappa statistic was used to measure the interrater reliability of multiple annotation results. Using the above corpus, we developed a machine-learning method to automatically classify these questions into one of the following six classes: Condition Management, Healthy Lifestyle, Diagnosis, Health Provider Choice, Treatment, and Epidemiology.ResultsThe consumer health question schema was developed with a four-hierarchical-level of specificity, comprising 48 quaternary categories and 35 annotation rules. The 2000 sample questions were coded with 2000 major codes and 607 minor codes. Using natural language processing techniques, we expressed the Chinese questions as a set of lexical, grammatical, and semantic features. Furthermore, the effective features were selected to improve the question classification performance. From the 6-category classification results, we achieved an average precision of 91.41%, recall of 89.62%, and F1 score of 90.24%.ConclusionsIn this study, we developed an automatic method to classify questions related to Chinese health care posted by the general public. It enables Artificial Intelligence (AI) agents to understand Internet users’ information needs on health care.

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“…This could be explained by the different types of data leading to wrong internal conceptualizations of medical terms and questions in the deep neural layers. This performance drop could also be caused by the complexity of the test consumer health questions that are often composed of several subquestions, contain contextual information, and may contain misspellings and ungrammatical sentences, which makes them more difficult to process [42]. Another aspect is the semantics of the task as discussed in Section 2.1.…”

Section: Discussion Of Rqe Resultsmentioning

confidence: 99%

A question-entailment approach to question answering

2019

Self Cite

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BackgroundOne of the challenges in large-scale information retrieval (IR) is developing fine-grained and domain-specific methods to answer natural language questions. Despite the availability of numerous sources and datasets for answer retrieval, Question Answering (QA) remains a challenging problem due to the difficulty of the question understanding and answer extraction tasks. One of the promising tracks investigated in QA is mapping new questions to formerly answered questions that are “similar”.ResultsWe propose a novel QA approach based on Recognizing Question Entailment (RQE) and we describe the QA system and resources that we built and evaluated on real medical questions. First, we compare logistic regression and deep learning methods for RQE using different kinds of datasets including textual inference, question similarity, and entailment in both the open and clinical domains. Second, we combine IR models with the best RQE method to select entailed questions and rank the retrieved answers. To study the end-to-end QA approach, we built the MedQuAD collection of 47,457 question-answer pairs from trusted medical sources which we introduce and share in the scope of this paper. Following the evaluation process used in TREC 2017 LiveQA, we find that our approach exceeds the best results of the medical task with a 29.8% increase over the best official score.ConclusionsThe evaluation results support the relevance of question entailment for QA and highlight the effectiveness of combining IR and RQE for future QA efforts. Our findings also show that relying on a restricted set of reliable answer sources can bring a substantial improvement in medical QA.

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Interactive use of online health resources: a comparison of consumer and professional questions

Cited by 39 publications

References 22 publications

Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach

Enriching consumer health vocabulary through mining a social Q&A site: A similarity-based approach

Classifying Chinese Questions Related to Health Care Posted by Consumers Via the Internet

A question-entailment approach to question answering

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