Trends in medical information retrieval on internet

Baujard, Olivier; Baujard, Vincent; Aurel, S; Boyer, Célia; Appel, Ron D.

doi:10.1016/s0010-4825(98)00036-5

Cited by 33 publications

(11 citation statements)

References 9 publications

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“…We adopted an agent software strategy (Baujard, Baujard, Aurel, Boyer, & Appel, 1998;Boyer et al, 1997;Chen, Soong, Grimes, & Orthner, 2004;Gao & Wang, 2004) to search various information sources automatically. The objective was not to develop new agents but to use available ones for searching and retrieving with minimum human intervention.…”

Section: Storage and Agent Softwarementioning

confidence: 99%

A Guided Approach for Personalized Information Search and Visualization

Chen

Soong

et al. 2008

IJIKM

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Our earlier study (Chen, Orthner, & Sell, 2005) showed that it is possible to record the searching strategy, to search and retrieve literature information automatically, and to visualize the retrieved information through a Web interface. The objective of this study was to update the current system into a prototype of the Literature and Information Tracking System to search, retrieve and visualize information in a guided and customized manner. The implementation of the system showed that users could update their account information and searching strategies including topics, online sources, etc., over Web interfaces. The guided approaches for optimized terms usage enhanced the recall, precision, and efficiency. The web-based system may become an efficient tool for both bench scientists and clinicians for their daily information retrieval and visualization. The implemented digital certification kept both convenient access and the highest possible level of security for the retrieved information. The system has great potential to serve a large number of concurrent users.

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Section: Storage and Agent Softwarementioning

confidence: 99%

A Guided Approach for Personalized Information Search and Visualization

Chen

Soong

et al. 2008

IJIKM

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“…Web pages are then compared with a set of relevant documents, and those with a similarity score above a certain threshold are considered relevant (Baujard et al, 1998).…”

Section: Web Page Filteringmentioning

confidence: 99%

“…An automatic approach was adopted to extract all the terms from a page and compare them with a domain lexicon, similarly to the method used in Baujard et al (1998). Both the number of relevant terms that appear in the page title and the TFIDF scores of the terms that appear in the body of the page were considered.…”

Section: Page Contentmentioning

confidence: 99%

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Searching and mining the web for personalized and specialized information

Chau

2005

SIGIR Forum

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With the rapid growth of the Web, users are often faced with the problem of information overload and find it difficult to search for relevant and useful information on the Web. Besides general-purpose search engines, there exist some alternative approaches that can help users perform searches on the Web more effectively and efficiently. Personalized search agents and specialized search engines are two such approaches. The goal of this research is to investigate how machine learning and artificial intelligence techniques can be used to improve these approaches.

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“…In general, the filtering techniques can be classified as follows: (1) domain experts manually determine the relevance of each Web page (e.g., Yahoo); (2) the relevance of a Web page is determined by the occurrences of particular keywords (e.g., computer) [6]; (3) TFIDF (term frequency * inverse document frequency) is calculated based on a lexicon created by domain-experts and Web pages are then with a high similarity score to the lexicon are considered relevant [1]; and (4) text classification techniques such as the Naive Bayesian classifier are applied [3,9]. Among these, text classification is the most promising approach.…”

Section: Related Workmentioning

confidence: 99%

Using Content-Based and Link-Based Analysis in Building Vertical Search Engines

Chau

Chen

2004

Digital Libraries: International Collaboration and Cross-Fertilization

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Abstract. This paper reports our research in the Web page filtering process in specialized search engine development. We propose a machine-learning-based approach that combines Web content analysis and Web structure analysis. Instead of a bag of words, each Web page is represented by a set of contentbased and link-based features, which can be used as the input for various machine learning algorithms. The proposed approach was implemented using both a feedforward/backpropagation neural network and a support vector machine. An evaluation study was conducted and showed that the proposed approaches performed better than the benchmark approaches.

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Trends in medical information retrieval on internet

Cited by 33 publications

References 9 publications

A Guided Approach for Personalized Information Search and Visualization

A Guided Approach for Personalized Information Search and Visualization

Searching and mining the web for personalized and specialized information

Using Content-Based and Link-Based Analysis in Building Vertical Search Engines

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