HelpfulMed: Intelligent searching for medical information over the internet

Chen, Hsinchun; Lally, Ann; Zhu, Bin; Chau, Michael

doi:10.1002/asi.10260

Cited by 52 publications

(31 citation statements)

References 35 publications

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“…The proposed Web-feature-based approaches are codenamed Approach 1 (for the neural network classifier) and Approach 2 (for the SVM classifier) in our experiment. A set of 1,000 documents were randomly selected from a medical testbed created in our previous research [4,5]. A medical lexicon, created based on the metathesaurus of the Unified Medical Language System (UMLS), was also used in our experiment.…”

Section: Discussionmentioning

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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Section: Discussionmentioning

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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“…He thought only mappings based on a much more specific set of seed terms, e.g., the ecology of a particular species of African millipede, would have much value for him and his students. This is a criticism with which many people might agree, and progress in bibliographic visualizations like ours may well lie in adding capabilities to map specific natural-language ''co-words'' from the titles, abstracts, or full texts of documents (8,26,27). Possibly the chief beneficiaries of MeSH (or other controlledvocabulary) mapping will be neither beginners nor subject experts, but ''in-between'' persons, such as librarians, subject indexers, science writers, journal editors, and teachers as they browse the many research areas to which they come as outsiders.…”

Section: Examples From Population Geneticsmentioning

confidence: 96%

“…ref. 8). The data are initially processed by our NOAH indexing engine, a specialized database application we designed for fast computations with verbal co-occurrence data (9).…”

mentioning

confidence: 99%

User-controlled mapping of significant literatures

White

Lin

Buzydlowski

et al. 2004

Proc. Natl. Acad. Sci. U.S.A.

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We apply a version of our web-based literature-mapping system to PNAS for 1971-2002, as indexed by the National Library of Medicine and the Institute for Scientific Information. Given a single input term from a user, a medical subject heading, a cocited author, or a cocited journal, PNASLINK rapidly displays views in which that term and the other 24 terms that most frequently co-occur with it in a bibliographic database are interrelated in ways suggesting fruitful combinations for document retrieval. The interrelationships are produced by two algorithms, pathfinder networks and Kohonen-style self-organizing maps. PNASLINK displays are themselves interactive interfaces that can retrieve documents from digital libraries (e.g., PNAS Online). This style of visualizing knowledge domains is called ''localized'' because it does not attempt to map the indexing of literatures in full but concentrates on the top terms in an ''associative thesaurus'' reflecting user interests. It also permits swift remappings, as the user recognizes terms worth pursuing. PNASLINK is illustrated with maps drawn from the literature of population genetics. Some comparative and evaluative comments are added, one from a domain expert indicating that the face validity of the system may be tempered by insufficient specificity in the indexing terms being mapped.

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“…For a SOM in a medical domain such as (Chen et al, 2003), noun phrases could be the appropriate semantic feature; however, for a SOM which provides access to news magazines (Rauber and Merkl, 1999), it would be more appropriate to select named entities (i.e. names of people, places, or things).…”

Section: The Domain and The Semantic Featuresmentioning

confidence: 99%

Improving self‐organising information maps as navigational tools: a semantic approach

Lin

Brusilovsky²,

He³

2011

Online Information Review

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• PurposeThe goal of the research is to explore whether the use of higher-level semantic features can help us build better SOM representation as measured from a human-centered perspective. We also explore an automatic evaluation method that utilizes human expert knowledge encapsulated in the structure of traditional textbooks to determine map representation quality.• Design/methodology/approach Two types of document representations involving semantic features have been explored: 1) using only one individual semantic feature, and 2) combining a semantic feature with keywords. A set of experiments were conducted to investigate the impact of semantic representation quality on the map. The experiments were performed on data collections that included single book corpus and multiple book corpus.• FindingsCombining keywords with certain semantic features achieves significant improvement of representation quality over the keywords-only approach in a relatively homogeneous single book corpus. Changing the ratios of the combined different features also affects the performance.While semantic mixtures can work well in single book corpus, they lose their increased effectiveness over keywords in the multiple-book corpus. This raises a concern about whether the semantic representations in the multiple book corpus are homogeneous and coherent enough to apply semantic features. The terminology issue among textbooks negatively impacts the ability of the SOM to generate a high quality map for heterogeneous collections.• Originality/valueWe explored the use of higher-level document representation features for the development of better-quality SOM. In addition, we piloted a specific method for evaluating the SOM quality based on the organization of information content in the map. IntroductionInformation maps (Kohonen, 1982) are becoming popular as interfaces to view and access large data collections such as digital libraries (DL). Unlike traditional search-based access, which provides selective and fragmented access to information, information maps allow users to comprehend large collections, to focus on the most interesting parts, and to explore specific resources in the context of their relationships to other resources and the library. Properties of information maps make them an excellent complement to search and browsing interfaces for DL. A recent study comparing student use of search, browsing and information map interfaces in an educational DL found that information maps were the method most preferred by students for accessing information; they were four times more popular than traditional search-based access methods. Several kinds of maps have been explored as interfaces to access large collections of resources (Börner and Chen, 2002, Yang et al., 2003, Dang et al., 2009, Perugini et al., 2004. Among these approaches, A self-organizing Map (SOM) (Kohonen, 1982) is frequently considered to be the most promising mapping approach for large document collections. While being most popular as a tool for two-dimensional cl...

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HelpfulMed: Intelligent searching for medical information over the internet

Cited by 52 publications

References 35 publications

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

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

User-controlled mapping of significant literatures

Improving self‐organising information maps as navigational tools: a semantic approach

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