Choices in Knowledge-Base Retrieval for Consumer Health Search

Jimmy,; Zuccon, Guido; Koopman, Bevan

doi:10.1007/978-3-319-76941-7_6

Cited by 6 publications

(12 citation statements)

References 8 publications

(13 reference statements)

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“…In particular, concept-based query expansion has been shown to be affected by the risk of introducing noise within the reformulated query and that gains are possible if methods are finely tuned; this was found for methods evaluated within the same task considered here [25,38,60] and within other health-related tasks, such as cohort selection/health record search [1,32,35,74,72,77,75] and clinical decision support [11,56].…”

Section: Effectiveness Of Query Expansionmentioning

confidence: 97%

An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014

et al. 2018

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Since its inception in 2013, one of the key contributions of the CLEF eHealth evaluation campaign has been the organization of an ad-hoc information retrieval (IR) benchmarking task. This IR task evaluates systems intended to support laypeople searching for and understanding health information. Each year the task provides registered participants with standard IR test collections consisting of a document collection and topic set. Participants then return retrieval results obtained by their IR systems for each query, which are assessed using a pooling procedure. In this article we focus on CLEF eHealth 2013 and 2014's retrieval task, which saw topics created based on patients' information needs associated with their medical discharge summaries.We overview the task and datasets created, and the results obtained by participating teams over these two years. We then provide a detailed comparative analysis of the results, and conduct an evaluation of the datasets in the light of these results. This two-fold study of the evaluation campaign teaches us about technical aspects of medical IR, such as the effectiveness of query expansion; the quality and characteristics of CLEF eHealth IR datasets, such as their reliability; and how to run an IR evaluation campaign in the medical domain.

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Section: Effectiveness Of Query Expansionmentioning

confidence: 97%

An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014

et al. 2018

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“…While some of this material is covered in an existing study (Jimmy et al 2018), this articles includes the following additional contributions:…”

Section: Key Contributionsmentioning

confidence: 99%

“…In constrast, Wikipedia is a crowdsourced, general purpose KB allowing people to promote and describe new concepts or augment existing concepts. While general purposes, Wikipedia contains considerable and detailed health information that has been effectively used in health related information retrieval (Jimmy et al 2018;Soldaini et al 2015).…”

Section: Consumer Health Search (Chs)mentioning

confidence: 99%

“…In an earlier study, we evaluated several design choices to instantiate the EQFE model in CHS (Jimmy et al 2018). These were:…”

Section: Consumer Health Search (Chs)mentioning

confidence: 99%

“…These choices were used for all KBs. For ME-CHV, we used the CHV version included in the UMLS version 2017AB [while in our previous work we used CHV version 20110204 (Jimmy et al 2018)].…”

Section: Me-allmentioning

confidence: 99%

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Payoffs and pitfalls in using knowledge-bases for consumer health search

2018

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Consumer health search (CHS) is a challenging domain with vocabulary mismatch and considerable domain expertise hampering peoples' ability to formulate effective queries. We posit that using knowledge bases for query reformulation may help alleviate this problem. How to exploit knowledge bases for effective CHS is nontrivial, involving a swathe of key choices and design decisions (many of which are not explored in the literature). Here we rigorously empirically evaluate the impact these different choices have on retrieval effectiveness. A state-of-the-art knowledge-base retrieval model-the Entity Query Feature Expansion model-was used to evaluate these choices, which include: which knowledge base to use (specialised vs. general purpose), how to construct the knowledge base, how to extract entities from queries and map them to entities in the knowledge base, what part of the knowledge base to use for query expansion, and if to augment the knowledge base search process with relevance feedback. While knowledge base retrieval has been proposed as a solution for CHS, this paper delves into the finer details of doing this effectively, highlighting both payoffs and pitfalls. It aims to provide some lessons to others in advancing the state-of-the-art in CHS.

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Towards an Aspect-Based Ranking Model for Clinical Trial Search

Roy

Rudra

Agrawal

et al. 2019

Lecture Notes in Computer Science

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Clinical Trials are crucial for the practice of evidence-based medicine. It provides updated and essential health-related information for the patients. Sometimes, Clinical trials are the first source of information about new drugs and treatments. Different stakeholders, such as trial volunteers, trial investigators, and meta-analyses researchers often need to search for trials. In this paper, we propose an automated method to retrieve relevant trials based on the overlap of UMLS concepts between the user query and clinical trials. However, different stakeholders may have different information needs, and accordingly, we rank the retrieved clinical trials based on the following four aspects -Relevancy, Adversity, Recency, and Popularity. We aim to develop a clinical trial search system which covers multiple disease classes, instead of only focusing on retrieval of oncology-based clinical trials. We follow a rigorous annotation scheme and create an annotated retrieval set for 25 queries, across five disease categories. Our proposed method performs better than the baseline model in almost 90% cases. We also measure the correlation between the different aspect-based ranking lists and observe a high negative Spearman rank's correlation coefficient between popularity and recency.

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Choices in Knowledge-Base Retrieval for Consumer Health Search

Cited by 6 publications

References 8 publications

An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014

An analysis of evaluation campaigns in ad-hoc medical information retrieval: CLEF eHealth 2013 and 2014

Payoffs and pitfalls in using knowledge-bases for consumer health search

Towards an Aspect-Based Ranking Model for Clinical Trial Search

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