Promoting Ranking Diversity for Biomedical Information Retrieval Using Wikipedia

Yin, Xiaoshi; Huang, Xiangji; Li, Zhoujun

doi:10.1007/978-3-642-12275-0_43

Cited by 21 publications

(16 citation statements)

References 13 publications

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“…In the IR community, Yin et al proposed a method to find passages to cover different aspects of a query in the biomedical domain [34]. These authors used Wikipedia and the TREC Genomics to demonstrate the effectiveness of their IR proposal.…”

Section: Related Workmentioning

confidence: 99%

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

Macedo

Pollettini

Baranauskas

et al. 2016

Journal of Biomedical Informatics

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A software framework can reduce costs related to the development of an application because it allows developers to reuse both design and code. Recently, companies and research groups have announced that they have been employing health software frameworks. This paper presents the design, proof-of-concept implementations and experimentation of the Health Surveillance Software Framework (HSSF). The HSSF is a framework that tackles the demand for the recommendation of surveillance information aiming at supporting preventive healthcare strategies. Examples of such strategies are the automatic recommendation of surveillance levels to patients in need of healthcare and the automatic recommendation of scientific literature that elucidates epigenetic problems related to patients. HSSF was created from two systems we developed in our previous work on health surveillance systems: the Automatic-SL and CISS systems. The Automatic-SL system aims to assist healthcare professionals in making decisions and in identifying children with developmental problems. The CISS service associates genetic and epigenetic risk factors related to chronic diseases with patient's clinical records. Towards evaluating the HSSF framework, two new systems, CISS+ and CISS-SW, were created by means of abstractions and instantiations of the framework (design and code). We show that HSSF supported the development of the two new systems given that they both recommend scientific papers using medical records as queries even though they exploit different computational technologies. In an experiment using simulated patients' medical records, we show that CISS, CISS+, and CISS-SW systems recommended more closely related and somewhat related documents than Google, Google Scholar and PubMed. Considering recall and precision measures, CISS+ surpasses CISS-SW in terms of precision.

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Section: Related Workmentioning

confidence: 99%

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

Macedo

Pollettini

Baranauskas

et al. 2016

Journal of Biomedical Informatics

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“…Kanhabua and Nørvåg (2010) propose a number of methods to determine the time of queries using temporal language models and shows how to increase the retrieval effectiveness by using the determined time of queries to re-rank the search results. Yin, Huang, and Li (2010) propose a costbased reranking method to promote diversity for biomedical IR. In the biomedicine domain, there are two works which focus on the diversity aspect.…”

Section: Rerankingmentioning

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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“…Yin et al [21] proposed a method to find passages to cover different aspects of a query in the biomedical domain. They have used Wikipedia and TREC 2006 and 2007 Genomics to demonstrate the effectiveness of their proposal.…”

Section: Related Workmentioning

confidence: 99%

Surveillance for the prevention of chronic diseases through information association

et al. 2014

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BackgroundResearch on Genomic medicine has suggested that the exposure of patients to early life risk factors may induce the development of chronic diseases in adulthood, as the presence of premature risk factors can influence gene expression. The large number of scientific papers published in this research area makes it difficult for the healthcare professional to keep up with individual results and to establish association between them. Therefore, in our work we aim at building a computational system that will offer an innovative approach that alerts health professionals about human development problems such as cardiovascular disease, obesity and type 2 diabetes.MethodsWe built a computational system called Chronic Illness Surveillance System (CISS), which retrieves scientific studies that establish associations (conceptual relationships) between chronic diseases (cardiovascular diseases, diabetes and obesity) and the risk factors described on clinical records. To evaluate our approach, we submitted ten queries to CISS as well as to three other search engines (Google™, Google Scholar™ and Pubmed®;) — the queries were composed of terms and expressions from a list of risk factors provided by specialists.ResultsCISS retrieved a higher number of closely related (+) and somewhat related (+/-) documents, and a smaller number of unrelated (-) and almost unrelated (-/+) documents, in comparison with the three other systems. The results from the Friedman’s test carried out with the post-hoc Holm procedure (95% confidence) for our system (control) versus the results for the three other engines indicate that our system had the best performance in three of the categories (+), (-) and (+/-). This is an important result, since these are the most relevant categories for our users.ConclusionOur system should be able to assist researchers and health professionals in finding out relationships between potential risk factors and chronic diseases in scientific papers.

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Promoting Ranking Diversity for Biomedical Information Retrieval Using Wikipedia

Cited by 21 publications

References 13 publications

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

A Health Surveillance Software Framework to deliver information on preventive healthcare strategies

Stochastic reranking of biomedical search results based on extracted entities

Surveillance for the prevention of chronic diseases through information association

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