DisArticle: a web server for SVM-based discrimination of articles on traditional medicine.

BACKGROUND: Much research has been done in Northeast Asia to show the efficacy of traditional medicine. While MEDLINE contains many biomedical articles including those on traditional medicine, it does not categorize those articles by specific research area. The aim of this study was to provide a method that searches for articles only on traditional medicine in Northeast Asia, including traditional Chinese medicine, from among the articles in MEDLINE. RESULTS: This research established an SVM-based classifier model to identify articles on traditional medicine. The TAK + HM classifier, trained with the features of title, abstract, keywords, herbal data, and MeSH, has a precision of 0.954 and a recall of 0.902. In particular, the feature of herbal data significantly increased the performance of the classifier. By using the TAK + HM classifier, a total of about 108,000 articles were discriminated as articles on traditional medicine from among all articles in MEDLINE. We also built a web server called DisArticle ( http://informatics.kiom.re.kr/disarticle ), in which users can search for the articles and obtain statistical data. CONCLUSIONS: Because much evidence-based research on traditional medicine has been published in recent years, it has become necessary to search for articles on traditional medicine exclusively in literature databases. DisArticle can help users to search for and analyze the research trends in traditional medicine.

Multi-focus cluster labeling.

Document collections resulting from searches in the biomedical literature, for instance, in PubMed, are often so large that some organization of the returned information is necessary. Clustering is an efficient tool for organizing search results. To help the user to decide how to continue the search for relevant documents, the content of each cluster can be characterized by a set of representative keywords or cluster labels. As different users may have different interests, it can be desirable with solutions that make it possible to produce labels from a selection of different topical categories. We therefore introduce the concept of multi-focus cluster labeling to give users the possibility to get an overview of the contents through labels from multiple viewpoints. The concept for multi-focus cluster labeling has been established and has been demonstrated on three different document collections. We illustrate that multi-focus visualizations can give an overview of clusters along axes that general labels are not able to convey. The approach is generic and should be applicable to any biomedical (or other) domain with any selection of foci where appropriate focus vocabularies can be established. A user evaluation also indicates that such a multi-focus concept is useful.

MEDLINE sólo es el primer paso / No disponible

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MScanner: a classifier for retrieving Medline citations.

BACKGROUND: Keyword searching through PubMed and other systems is the standard means of retrieving information from Medline. However, ad-hoc retrieval systems do not meet all of the needs of databases that curate information from literature, or of text miners developing a corpus on a topic that has many terms indicative of relevance. Several databases have developed supervised learning methods that operate on a filtered subset of Medline, to classify Medline records so that fewer articles have to be manually reviewed for relevance. A few studies have considered generalisation of Medline classification to operate on the entire Medline database in a non-domain-specific manner, but existing applications lack speed, available implementations, or a means to measure performance in new domains. RESULTS: MScanner is an implementation of a Bayesian classifier that provides a simple web interface for submitting a corpus of relevant training examples in the form of PubMed IDs and returning results ranked by decreasing probability of relevance. For maximum speed it uses the Medical Subject Headings (MeSH) and journal of publication as a concise document representation, and takes roughly 90 seconds to return results against the 16 million records in Medline. The web interface provides interactive exploration of the results, and cross validated performance evaluation on the relevant input against a random subset of Medline. We describe the classifier implementation, cross validate it on three domain-specific topics, and compare its performance to that of an expert PubMed query for a complex topic. In cross validation on the three sample topics against 100,000 random articles, the classifier achieved excellent separation of relevant and irrelevant article score distributions, ROC areas between 0.97 and 0.99, and averaged precision between 0.69 and 0.92. CONCLUSION: MScanner is an effective non-domain-specific classifier that operates on the entire Medline database, and is suited to retrieving topics for which many features may indicate relevance. Its web interface simplifies the task of classifying Medline citations, compared to building a pre-filter and classifier specific to the topic. The data sets and open source code used to obtain the results in this paper are available on-line and as supplementary material, and the web interface may be accessed at http://mscanner.stanford.edu.

The effect of feature representation on MEDLINE document classification.

This work explores the effect of text representation techniques on the overall performance of medical text classification. To accomplish this goal, we developed a text classification system that supports the very basic word representation (bag-of-words) and the more complex medical phrase representation (bag-of-phrases). We also combined word and phrase representations (hybrid) for further analysis. Our system extracts medical phrases from text by incorporating a medical knowledge base and natural language processing techniques. We conducted experiments to evaluate the effects of different representations by measuring the change in classification performance with MEDLINE documents from the OHSUMED dataset. We measured classification performance with information retrieval metrics; precision (p), recall (r), and F1-score (F1). In our experiments, we achieved better classification performance with the hybrid approach (p=0.87, r=0.46, F1=0.60) compared to the bag-of-words approach (p=0.85, r=0.44, F1=0.58) and the bag-of-phrases approach (p=0.87, r=0.42, F1=0.57).

La autocitación en la revista radiología: un parámetro de calidad / Rate of self-referencing in the journal Radiología: An indicator of quality

Objetivo: La autocitación, considerada como el número de veces que una revista científica se cita a sí misma en las referencias bibliográicas de sus artículos, es un importante criterio de calidad.Nuestro objetivo es analizar la autocitación en la revista Radiología.Material y método: Se analizaron los artículos publicados en Radiología durante el período 1994-1998, calculando el Índice de citas a Radiología (ICR) y el Índice de artículos que citan a Radiología (IAR). Estos índices se calcularon también en European Radiology (Índice de citas a European Radiology ICER- e Índice de artículos que citan a European Radiology IAER-), revista incluida en el Index Medicus, durante un año (1998).Resultados: El ICR en el período 1994-1998 osciló entre el 1,5 por ciento y el 1,9 por ciento. En 1998 el ICR fue del 1,8 por ciento mientras que el ICER fue sólo del 0,9 por ciento. Las diferencias entre el ICR e ICER fueron estadísticamente significativas (p = 0,02). Con referencia al IAR en 1998, este fue también superior al obtenido en European Radiology (IAR: 2,3 por ciento frente a IAER: 1,6 por ciento).Conclusiones: Los autores que publican en Radiología tienen un índice más elevado de autocitación que los que lo hacen en European Radiology, estando esta última revista incluida en el Index Medicus y su Base de Datos Medline (AU)

Criteria for evaluating alternative MEDLINE search engines.

With so many options available for searching MEDLINE on the World Wide Web or as a component of an online service, evaluation criteria are suggested as a means of assisting librarians in determining the positive and negative aspects of alternative MEDLINE sites. A set of searches was utilized to systematically compare MEDLINE sites. Sites evaluated included Avicenna, America Online, HealthGate, PubMed, Medscape, and Physicians' Online. Some features used to evaluate these sites were: default fields; operators (default); access to MeSH; subheadings; stop words protected in MeSH; truncation; and stemming. This article will describe the group process used to arrive at the evaluation criteria, as well as some general conclusions which will help librarians in directing their users to a particular MEDLINE site.