Exploring the use of concept spaces to improve medical information retrieval

Houston, Andrea L.; Chen, Hsinchun; Schatz, Bruce R.; Hubbard, Susan M.; Sewell, Robin; Ng, Tsz‐Wai

doi:10.1016/s0167-9236(00)00097-x

Cited by 34 publications

(19 citation statements)

References 15 publications

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“…[2] uses only a set of lists whose quality is high and contains a large number of terms. However, many lists that are shorter and may contain more heterogeneous pieces of information exist on the web.…”

Section: Methodsmentioning

confidence: 99%

Building Large Collections of Chinese and English Medical Terms from Semi-Structured and Encyclopedia Websites

Sun

Zhang

et al. 2013

PLoS ONE

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To build large collections of medical terms from semi-structured information sources (e.g. tables, lists, etc.) and encyclopedia sites on the web. The terms are classified into the three semantic categories, Medical Problems, Medications, and Medical Tests, which were used in i2b2 challenge tasks. We developed two systems, one for Chinese and another for English terms. The two systems share the same methodology and use the same software with minimum language dependent parts. We produced large collections of terms by exploiting billions of semi-structured information sources and encyclopedia sites on the Web. The standard performance metric of recall (R) is extended to three different types of Recall to take the surface variability of terms into consideration. They are Surface Recall (), Object Recall (), and Surface Head recall (). We use two test sets for Chinese. For English, we use a collection of terms in the 2010 i2b2 text. Two collections of terms, one for English and the other for Chinese, have been created. The terms in these collections are classified as either of Medical Problems, Medications, or Medical Tests in the i2b2 challenge tasks. The English collection contains 49,249 (Problems), 89,591 (Medications) and 25,107 (Tests) terms, while the Chinese one contains 66,780 (Problems), 101,025 (Medications), and 15,032 (Tests) terms. The proposed method of constructing a large collection of medical terms is both efficient and effective, and, most of all, independent of language. The collections will be made publicly available.

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Section: Methodsmentioning

confidence: 99%

Building Large Collections of Chinese and English Medical Terms from Semi-Structured and Encyclopedia Websites

Sun

Zhang

et al. 2013

PLoS ONE

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“…Houston [10] compared 3 different thesauri for medical QE, consisting of corpus terms, MESH terms, and UMLS terms. He found no significant difference between them, but also little overlap, supporting a case for further exploration to discover the significant features of each term set, and use this information to combine their strengths.…”

Section: Related Workmentioning

confidence: 99%

The effect of using domain specific ontologies in query expansion in medical field

Jalali

Borujerdi

2008

2008 International Conference on Innovations in Information Technology

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Domain specific ontologies can be used to improve both precision and recall of information retrieval systems. One approach in this regard is using query expansion techniques and the other would be introducing a semantic similarity measure for concepts in ontology. Although each approach has its own benefits and drawbacks, query expansion techniques are preferred when the corpus volume is so huge that examining concept pairs between query and documents is not reasonable. In this paper a semantic query expansion algorithm for medical information retrieval is introduced. Proposed approach consists of identifying MeSH (Medical Subject Headings) concepts in user's query and applying expansion algorithm to them. Expansion algorithm is based on the location of concepts in MeSH hierarchy, number of synonyms of each concept and number of terms the concept is made of. Results show improvements over classic method, query expansion using general purpose ontology and a number of other approaches.

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“…To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. JCDL'02, July [13][14][15][16][17]2002 according to the information sources utilized by the system, which they described as "community inputs". These include item attribute, external item popularity, purchase history, ratings, text comments and context/process information.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Since it has been shown that an asymmetric algorithm performs better than other clustering function like cosine function [13] to compute similarity for term associations, extending it to books is expected to improve precision as well. • tf ijk = tf ij when descriptor i appears in both book j and k , otherwise tf ijk = 0…”

Section: Similarity Computationmentioning

confidence: 99%

See 1 more Smart Citation

A graph-based recommender system for digital library

Huang

Chung

Ong

et al. 2002

Proceedings of the 2nd ACM/IEEE-CS Joint Conference on Digital Libraries

Self Cite

158

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Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, useruser and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.

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Exploring the use of concept spaces to improve medical information retrieval

Cited by 34 publications

References 15 publications

Building Large Collections of Chinese and English Medical Terms from Semi-Structured and Encyclopedia Websites

Building Large Collections of Chinese and English Medical Terms from Semi-Structured and Encyclopedia Websites

The effect of using domain specific ontologies in query expansion in medical field

A graph-based recommender system for digital library

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