Exploiting semantics for improving clinical information retrieval

Babashzadeh, Atanaz; Huang, Jimmy Xiangji; Daoud, Mariam

doi:10.1145/2484028.2484167

Cited by 13 publications

(8 citation statements)

References 21 publications

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“…Specifically, the original query submitted by a user is automatically expanded with other words that best capture the actual user intent, or that simply produce a more useful query, that is, a query that is more likely to retrieve relevant documents. In the biomedicine domain, Babashzadeh, Huang, and Daoud (2013) use semantic information to improve the performance of clinical IR systems by representing queries in an expressive context. Carpineto and Romano (2012) survey approaches of automatic query expansion.…”

Section: Automatic Query Expansionmentioning

confidence: 99%

Stochastic reranking of biomedical search results based on extracted entities

Fafalios

Tzitzikas

2017

Asso for Info Science & Tech

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Health-related information is nowadays accessible from many sources and is one of the most searched-for topics on the Internet. However, existing search systems often fail to provide users with a good list of medical search results, especially for classic (keyword-based) queries. In this article we elaborate on whether and how we can exploit biomedicine-related entities from the emerging Web of Data for improving (through reranking) the results returned by a search system. The aim is to promote relevant but low-ranked hits containing entities that are important to the current search context. We introduce an approach that is based on entity extraction applied on the retrieved documents, yielding a graph of documents along with entities, which in turn is analyzed probabilistically using a Random Walk-based method. The proposed approach is independent of the submitted query and the underlying retrieval models, and thus can be applied over any ranked list of medical search results. Evaluation results using the data set of TREC Clinical Decision Support track demonstrate that the proposed approach can significantly improve the results returned by classic and widely applicable retrieval models. The results also enabled us to identify cases where the proposed reranking method fails to improve the ranking.

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Section: Automatic Query Expansionmentioning

confidence: 99%

Stochastic reranking of biomedical search results based on extracted entities

Fafalios

Tzitzikas

2017

Asso for Info Science & Tech

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“…The use of query features in the IR domain has received much attention from researchers (Babashzadeh, Huang, & Daoud, 2013;Carmel & Tov, 2010;Hauff et al, 2009;Tamine et al, 2015). Query features can be grouped based on query terms, such as query specificity (which refers to the ability of the query to represent the current information need and discriminate it from others), query ambiguity (which groups features that measure the ambiguity of terms), and term relatedness (which allows measuring the semantic dependencies between terms).…”

Section: Related Work: Query Featuresmentioning

confidence: 99%

Mining correlations between medically dependent features and image retrieval models for query classification

Ayadi

Torjmen-Khemakhem

Daoud

et al. 2017

Asso for Info Science & Tech

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The abundance of medical resources has encouraged the development of systems that allow for efficient searches of information in large medical image data sets. State‐of‐the‐art image retrieval models are classified into three categories: content‐based (visual) models, textual models, and combined models. Content‐based models use visual features to answer image queries, textual image retrieval models use word matching to answer textual queries, and combined image retrieval models, use both textual and visual features to answer queries. Nevertheless, most of previous works in this field have used the same image retrieval model independently of the query type. In this article, we define a list of generic and specific medical query features and exploit them in an association rule mining technique to discover correlations between query features and image retrieval models. Based on these rules, we propose to use an associative classifier (NaiveClass) to find the best suitable retrieval model given a new textual query. We also propose a second associative classifier (SmartClass) to select the most appropriate default class for the query. Experiments are performed on Medical ImageCLEF queries from 2008 to 2012 to evaluate the impact of the proposed query features on the classification performance. The results show that combining our proposed specific and generic query features is effective in query classification.

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“…Recently, analysing query features has received high attention in the IR domain [3,6,11,14,31,34,36]. Several features are proposed and classified according to characteristics of the user information needs and the expressed query.…”

Section: Query Featuresmentioning

confidence: 99%

Correlating medical-dependent query features with image retrieval models using association rules

Ayadi

Torjmen

Daoud

et al. 2013

Proceedings of the 22nd ACM International Conference on Conference on Information &Amp; Knowledge Management - CIKM '13

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The increasing quantities of available medical resources have motivated the development of effective search tools and medical decision support systems. Medical image search tools help physicians in searching medical image datasets for diagnosing a disease or monitoring the stage of a disease given previous patient's image screenings. Image retrieval models are classified into three categories : contentbased (visual), textual and combined models. In most of previous work, a unique image retrieval model is applied for any user formulated query independently of what retrieval model best suits the information need behind the query. The main challenge in medical image retrieval is to cope the semantic gap between user information needs and retrieval models. In this paper, we propose a novel approach for finding correlations between medical query features and retrieval models based on association rule mining. We define new medical-dependent query features such as image modality and presence of specific medical image terminology and make use of existing generic query features such as query specificity, ambiguity and cohesiveness. The proposed query features are then exploited into association rule mining for discovering rules which correlate query features to visual, textual or combined image retrieval models. Based on the discovered rules, we propose to use an associative classifier that finds the best suitable rule with a maximum feature coverage for a new query. Experiments are performed on Image CLEF queries from 2008 to 2012 where we evaluate the impact of our proposed query features on the classification performance. Results show that combining our proposed specific and generic query features is effective for classifying queries. A comparative study between our classifier, CBA, Naïve Bayes, Bayes Net and decision trees showed that our best coverage associative classifier outperforms existing classifiers where it achieves an improvement of 30%.

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Exploiting semantics for improving clinical information retrieval

Cited by 13 publications

References 21 publications

Stochastic reranking of biomedical search results based on extracted entities

Stochastic reranking of biomedical search results based on extracted entities

Mining correlations between medically dependent features and image retrieval models for query classification

Correlating medical-dependent query features with image retrieval models using association rules

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