Thesaurus-based query and document expansion in conceptual indexing with UMLS: Application in medical information retrieval

Diem, Le Thi Hoang; Chevallet, Jean–Pierre; Thuy, Dong Thi Bich

doi:10.1109/rivf.2007.369163

Cited by 6 publications

(4 citation statements)

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“…We utilize β [1,10] scale in figures to explain the expected improvement. Specifically, we tried with β = [1,2,3,4,5,6,7,8,9,10], where each point is equal to the average performance of the 10 queries.…”

Section: ) Resultsmentioning

confidence: 99%

“…They goal is to append the original query with additional terms that are specifically relevant to the query's scenario. The exploration of UMLS knowledge is done by mapping a large text of collection ImageCLEFMed(CLEF-Cross Language Evaluation Forum) to UMLS concepts, and expanding queries and documents automatically base on semantic relations in the UMLS hierarchy [7]. The exploitation of semantic relations from a knowledge based is what most differ their work from ours.…”

Section: A Query Expansion In the Medical Domainmentioning

confidence: 99%

“…Lucene is a high performance, full featured text search engine library written in Java 7 . We utilize the processed indexing and content database to compute the tag neighborhood and calculate the neighbor frequency and weight as described in section III-A.…”

Section: ) Experimental Setupmentioning

confidence: 99%

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Using Tag-Neighbors for Query Expansion in Medical Information Retrieval

Durão

Bayyapu

et al. 2011

2011 International Conference on Information Science and Applications

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Abstract-In the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. To overcome the limitations of under-specified queries, we utilize tags to enhance information retrieval capabilities by expanding users' original queries with context-relevant information. We compute a set of significant tag neighbor candidates based on the neighbor frequency and weight, and utilize the most frequent and weighted neighbors to expand an entry query that has terms matching tags. The proposed approach is evaluated using MedWorm medical article collection and standard evaluation methods from the text retrieval conference (TREC). We compared the baseline of 0.353 for Mean Average Precision (MAP), reaching a MAP 0.491 (+39%) with the query expansion. In-depth analysis shows how this strategy is beneficial when compared with different ranks of the retrieval results. I. INTRODUCTIONIn the context of medical document retrieval, users often under-specified queries lead to undesired search results that suffer from not containing the information they seek, inadequate domain knowledge matches and unreliable sources. For instance, when a user wants to search for a recent outbreak of influenza on the web, a search with the query influenza will return a list of documents containing the query term, ranked by a set of criteria defined by the search engine. In this case, at least three issues may affect the quality of the search result. One, a query with only one or two terms may be under-specified, that is, it may not contain enough terms for the search engine to retrieve the desired information to the user. Second, in the document repository of the search engine, there might exist more than hundreds of thousands articles matching the requested query. In such an amount of information, it is impossible to locate the desired information by simply browsing through all contents of returned results. The third reason is related to domain knowledge requirements. Because conventional search engines focus on generic information search, domain specific results are usually not taken into consideration during the search. Thus, a simple word based search does not produce relevant search results in specific domains such as the medical domain [1]. As a consequence of these issues related to query-based searches, only one fourth to one half of the relevant articles on a given topic are retrieved in searches performed in specific domains [2]. In other words, the sparse and incomplete query terms may

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Section: ) Resultsmentioning

confidence: 99%

Section: A Query Expansion In the Medical Domainmentioning

confidence: 99%

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Using Tag-Neighbors for Query Expansion in Medical Information Retrieval

Durão

Bayyapu

et al. 2011

2011 International Conference on Information Science and Applications

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“…Representing concepts as continuous vectors, the method accumulated pairwise similarity among pairs of concepts to measure the semantic knowledge in documents. Some other works constructed and used semantic knowledge graph to improve efficiency and (or) effectiveness in medical data analytic and data mining [48,49]. Being enlightened by their success, semantic knowledge is also adopted in our work to help discover underlying patterns from the data.…”

Section: Knowledge Acquisition In Health Domainmentioning

confidence: 99%

Mining health knowledge graph for health risk prediction

et al. 2020

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Nowadays classification models have been widely adopted in healthcare, aiming at supporting practitioners for disease diagnosis and human error reduction. The challenge is utilising effective methods to mine real-world data in the medical domain, as many different models have been proposed with varying results. A large number of researchers focus on the diversity problem of real-time data sets in classification models. Some previous works developed methods comprising of homogeneous graphs for knowledge representation and then knowledge discovery. However, such approaches are weak in discovering different relationships among elements. In this paper, we propose an innovative classification model for knowledge discovery from patients' personal health repositories. The model discovers medical domain knowledge from the massive data in the National Health and Nutrition Examination Survey (NHANES). The knowledge is conceptualised in a heterogeneous knowledge graph. On the basis of the model, an innovative method is developed to help uncover potential diseases suffered by people and, furthermore, to classify patients' health risk. The proposed model is evaluated by comparison to a baseline model also built on the NHANES data set in an empirical experiment. The performance of proposed model is promising. The paper makes significant contributions to the advancement of knowledge in data mining with an innovative classification model specifically crafted for domain-based data. In addition, by accessing the

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Medical Information Retrieval Enhanced with User’s Query Expanded with Tag-Neighbors

Durão

Bayyapu

et al. 2013

Handbook of Medical and Healthcare Technologies

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Thesaurus-based query and document expansion in conceptual indexing with UMLS: Application in medical information retrieval

Cited by 6 publications

References 10 publications

Using Tag-Neighbors for Query Expansion in Medical Information Retrieval

Using Tag-Neighbors for Query Expansion in Medical Information Retrieval

Mining health knowledge graph for health risk prediction

Medical Information Retrieval Enhanced with User’s Query Expanded with Tag-Neighbors

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