The research of query expansion based on medical terms reweighting in medical information retrieval

Diao, Lijuan; Yan, Hong; Li, Fuxue; Song, Shoujun; Liu, Guohua; Wang, Rui

doi:10.1186/s13638-018-1124-3

Cited by 17 publications

(12 citation statements)

References 17 publications

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“…However, they found out that combining retrieval models with selective query term weighting, based on medical thesauri and physician feedback, proves effective to address these challenges and improves performances significantly. Similar findings were also obtained by Zhu et al [256] and Diao et al [62], that relied on query expansion and reweighting techniques to improve retrieval performances on medical records. Therefore, the design of effective tools to access and search textual medical information can benefit, among other things, from enhancing the query through expansion and/or rewriting techniques that leverage the information contained within external knowledge resources.…”

Section: 8 Chapter Outcomes and Lessons Learnedsupporting

confidence: 85%

Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

Marchesin

2021

SIGIR Forum

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In this thesis we tackle the semantic gap, a long-standing problem in Information Retrieval (IR). The semantic gap can be described as the mismatch between users' queries and the way retrieval models answer to such queries. Two main lines of work have emerged over the years to bridge the semantic gap: (i) the use of external knowledge resources to enhance the bag-of-words representations used by lexical models, and (ii) the use of semantic models to perform matching between the latent representations of queries and documents. To deal with this issue, we first perform an in-depth evaluation of lexical and semantic models through different analyses [Marchesin et al., 2019]. The objective of this evaluation is to understand what features lexical and semantic models share, if their signals are complementary, and how they can be combined to effectively address the semantic gap. In particular, the evaluation focuses on (semantic) neural models and their critical aspects. Each analysis brings a different perspective in the understanding of semantic models and their relation with lexical models. The outcomes of this evaluation highlight the differences between lexical and semantic signals, and the need to combine them at the early stages of the IR pipeline to effectively address the semantic gap. Then, we build on the insights of this evaluation to develop lexical and semantic models addressing the semantic gap. Specifically, we develop unsupervised models that integrate knowledge from external resources, and we evaluate them for the medical domain - a domain with a high social value, where the semantic gap is prominent, and the large presence of authoritative knowledge resources allows us to explore effective ways to address it. For lexical models, we investigate how - and to what extent - concepts and relations stored within knowledge resources can be integrated in query representations to improve the effectiveness of lexical models. Thus, we propose and evaluate several knowledge-based query expansion and reduction techniques [Agosti et al., 2018, 2019; Di Nunzio et al., 2019]. These query reformulations are used to increase the probability of retrieving relevant documents by adding to or removing from the original query highly specific terms. The experimental analyses on different test collections for Precision Medicine - a particular use case of Clinical Decision Support (CDS) - show the effectiveness of the proposed query reformulations. In particular, a specific subset of query reformulations allow lexical models to achieve top performing results in all the considered collections. Regarding semantic models, we first analyze the limitations of the knowledge-enhanced neural models presented in the literature. Then, to overcome these limitations, we propose SAFIR [Agosti et al., 2020], an unsupervised knowledge-enhanced neural framework for IR. SAFIR integrates external knowledge in the learning process of neural IR models and it does not require labeled data for training. Thus, the representations learned within this framework are optimized for IR and encode linguistic features that are relevant to address the semantic gap. The evaluation on different test collections for CDS demonstrate the effectiveness of SAFIR when used to perform retrieval over the entire document collection or to retrieve documents for Pseudo Relevance Feedback (PRF) methods - that is, when it is used at the early stages of the IR pipeline. In particular, the quantitative and qualitative analyses highlight the ability of SAFIR to retrieve relevant documents affected by the semantic gap, as well as the effectiveness of combining lexical and semantic models at the early stages of the IR pipeline - where the complementary signals they provide can be used to obtain better answers to semantically hard queries.

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Section: 8 Chapter Outcomes and Lessons Learnedsupporting

confidence: 85%

Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

Marchesin

2021

SIGIR Forum

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“…To increase the recall of relevant documents to the user query, we apply query expansion techniques using a COVID-specific ontology of standardized medical terms, their synonyms, classes, and subclasses engineered by clinical trial domain experts [ 20 ]. For instance, once the user searches for heparin, the query automatically expands to all 3 majors of heparin: unfractionated heparin, low-molecular-weight heparin, ultra-low-molecular weight heparin, and their trade names based on COVID-specific ontology (e.g., nadroparin, Fraxiparine, Fraxodi, Calciparine, bemiparin, Zibor, Ivor, enoxaparin, Clexane, Lovenox, Fragmin, Dalteparin, and dociparstat).…”

Section: Methodsmentioning

confidence: 99%

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development

et al. 2021

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Introduction: The SARS-CoV-2 pandemic has led to one of the most critical and boundless waves of publications in the history of modern science. The necessity to find and pursue relevant information and quantify its quality is broadly acknowledged. Modern information retrieval techniques combined with artificial intelligence (AI) appear as one of the key strategies for COVID-19 living evidence management. Nevertheless, most AI projects that retrieve COVID-19 literature still require manual tasks. Methods: In this context, we present a novel, automated search platform, called Risklick AI, which aims to automatically gather COVID-19 scientific evidence and enables scientists, policy makers, and healthcare professionals to find the most relevant information tailored to their question of interest in real time. Results: Here, we compare the capacity of Risklick AI to find COVID-19-related clinical trials and scientific publications in comparison with clinicaltrials.gov and PubMed in the field of pharmacology and clinical intervention. Discussion: The results demonstrate that Risklick AI is able to find COVID-19 references more effectively, both in terms of precision and recall, compared to the baseline platforms. Hence, Risklick AI could become a useful alternative assistant to scientists fighting the COVID-19 pandemic.

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“…the UMLS lexicon and UMLS Metathesaurus, in our approach. Therefore, rather than using a limited resource such as the previously introduced discharge summary reports or a generic source such as web search engines, we use a combination of medical knowledge bases (such as MeSH, SNOMED, RxNorm [36]) for semantic concept highlighting and expansion. In addition, we propose a heuristical approach for re-weighting medical query terms based on their mappings to their relevant medical concepts in the used resources and experimentally demonstrate its impact.…”

Section: Related Workmentioning

confidence: 99%

“…In this context, we submit all ngram tokens to the UMLS lexicon [20] in order to classify them into the four categories. In addition, we utilize the MetaMap tool to detect synonymous medical terms for any of the terms that fall under the three first categories based on the UMLS Metathesaurus; which includes data from MeSH, SNOMED, RxNorm, and other collections [36]. The multiple semantic resource based query processing scenario is formalized in Algorithm 1: To demonstrate these steps, we consider the following two example medical queries that are obtained from two different datasets (CLEF e-Health 2014 and TREC).…”

Section: A Query Processing and Umls-based Expansionmentioning

confidence: 99%

Head Concepts Selection for Verbose Medical Queries Expansion

et al. 2020

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Semantic concepts and relations encoded in domain-specific ontologies and other medical semantic resources play a crucial role in deciphering terms in medical queries and documents. The exploitation of these resources for tackling the semantic gap issue has been widely studied in the literature. However, there are challenges that hinder their widespread use in real-world applications. Among these challenges is the insufficient knowledge individually encoded in existing medical ontologies, which is magnified when users express their information needs using long-winded natural language queries. In this context, many of the users' query terms are either unrecognized by the used ontologies, or cause retrieving false positives that degrade the quality of current medical information search approaches. In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically-enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query-document matching process. To demonstrate the effectiveness of the proposed approach, we conducted several experiments over the CLEF e-Health 2014 dataset. Findings indicate that the proposed method combining several extrinsic semantic resources proved to be more effective than related approaches in terms of precision measure.

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The research of query expansion based on medical terms reweighting in medical information retrieval

Cited by 17 publications

References 17 publications

Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

Developing unsupervised knowledge-enhanced models to reduce the semantic gap in information retrieval

Utilizing Artificial Intelligence to Manage COVID-19 Scientific Evidence Torrent with Risklick AI: A Critical Tool for Pharmacology and Therapy Development

Head Concepts Selection for Verbose Medical Queries Expansion

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