Health Card Retrieval for Consumer Health Search

Jimmy,; Zuccon, Guido; Koopman, Bevan; Demartini, Gianluca

doi:10.1145/3357384.3358128

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…Ranking. Once MeSH terms have been retrieved, they are ranked according to the approach for entity ranking described by Jimmy et al [44] by adapting features proposed by Balog [45]. In total, we use eleven entity features.…”

Section: Lexical Mesh Term Suggestionmentioning

confidence: 99%

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

Wang¹,

Scells²,

Koopman³

et al. 2022

Preprint

Self Cite

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High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., the Medical Subject Headings (MeSH) thesaurus), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited.This article investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. In this context, we devise lexical and pre-trained language models based methods. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We further contribute an extensive analysis of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

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Section: Lexical Mesh Term Suggestionmentioning

confidence: 99%

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

Wang¹,

Scells²,

Koopman³

et al. 2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Once MeSH terms have been retrieved, they are ranked according to the approach for entity ranking described by Jimmy et al [10] by adapting features proposed by Balog [4]. In total, we use eleven features, each described in Table 1.…”

Section: Mesh Term Rankingmentioning

confidence: 99%

MeSH Term Suggestion for Systematic Review Literature Search

Wang

Scells

et al. 2021

Australasian Document Computing Symposium

Self Cite

View full text Add to dashboard Cite

High-quality medical systematic reviews require comprehensive literature searches to ensure the recommendations and outcomes are sufficiently reliable. Indeed, searching for relevant medical literature is a key phase in constructing systematic reviews and often involves domain (medical researchers) and search (information specialists) experts in developing the search queries. Queries in this context are highly complex, based on Boolean logic, include free-text terms and index terms from standardised terminologies (e.g., MeSH), and are difficult and time-consuming to build. The use of MeSH terms, in particular, has been shown to improve the quality of the search results. However, identifying the correct MeSH terms to include in a query is difficult: information experts are often unfamiliar with the MeSH database and unsure about the appropriateness of MeSH terms for a query. Naturally, the full value of the MeSH terminology is often not fully exploited.This paper investigates methods to suggest MeSH terms based on an initial Boolean query that includes only free-text terms. These methods promise to automatically identify highly effective MeSH terms for inclusion in a systematic review query. Our study contributes an empirical evaluation of several MeSH term suggestion methods. We perform an extensive analysis of the retrieval, ranking, and refinement of MeSH term suggestions for each method and how these suggestions impact the effectiveness of Boolean queries.

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“…Given the aforementioned benefits of patients' being proactive in searching their symptoms, some existing systems allow consumers to search for potential diagnoses. Examples of these systems include health cards, which provide disease information alongside search results (Jimmy et al, 2019), a curated multi-document answer synthesis that takes answers from an expert annotated database , and a search system that allows users to get expert-level recall using consumer queries over peer-reviewed COVID-19 literature (Nguyen et al, 2022). These systems are important and require continual research as consumers require high-quality medical advice for a broad range of topics.…”

Section: Introductionmentioning

confidence: 99%

MedRedQA for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines

Nguyen,

Karimi,

Rybinski

et al. 2023

Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacifi

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Medical question answering for consumers aims to assist consumers in finding trustworthy and relevant information for their concerns. Although some datasets exist for consumer question answering, they use synthetic questions or present difficult-to-understand answers. We introduce MedRedQA, a large non-factoid English consumer Question Answering (QA) dataset containing 51,000 pairs of consumer questions and their corresponding expert answers. MedRedQA facilitates research that aims to provide consumer-friendly responses to realworld consumer questions. We propose and benchmark three tasks for consumer medical question answering for our dataset, including (1) candidate answer ranking, (2) open-ended answer generation, and (3) answer generation with scientific evidence. Our benchmarking experiments reveal that, for the ranking task, it is feasible to retrieve expert answers within five responses in an oracle retrieval. Though, in an answer generation task, it remains challenging to align the generation toward expert answers. However, our experiments show that including scientific evidence in the prompt may reduce hallucinations in an answer generation setup. 1

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Health Card Retrieval for Consumer Health Search

Cited by 5 publications

References 10 publications

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

Automated MeSH Term Suggestion for Effective Query Formulation in Systematic Reviews Literature Search

MeSH Term Suggestion for Systematic Review Literature Search

MedRedQA for Medical Consumer Question Answering: Dataset, Tasks, and Neural Baselines

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