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Biomedical subjects

Ronald N Kostoff

Publications and source records attributed to Ronald N Kostoff.

6 recordsLinked to original sources
PubMed2005

The hidden structure of neuropsychology: text mining of the journal Cortex: 1991--2001.

BACKGROUND: The stated mission of Cortex is "the study of the inter-relations of the nervous system and behavior, particularly as these are reflected in the effects of brain lesions on cognitive functions." The purpose of this paper is to explore the relationship between the stated mission and the executed mission as reflected by the characteristics of papers published in Cortex. In addition, we examine whether the results and conclusions of an analysis of this kind are affected by the level of description of the published papers. OBJECTIVES: A) Identify characteristics of contributors to Cortex; B) Identify characteristics of those who cite Cortex; C) Identify recurring themes; D) Identify the relationships among the recurring themes; E) Compare recurring themes and determine their relationships to the mission of Cortex; F) Identify the sensitivity of these results to the level of description of the Cortex papers used as the source database. G) Compare Cortex characteristics with those of Neuropsychologia, another Europe-based international neuropsychology journal. METHODS: Text mining (extraction of useful information from text) was used to generate the characteristics of the journal Cortex. Bibliometrics provided the Cortex contributor infrastructure (author/ organization/ country/ citation distributions), and computational linguistics identified the recurring technical themes and their inter-relationships. Citation mining (the integration of citation bibliometrics and text mining) was used to profile the research user community. Four levels of published article description were compared for the analysis: Full Text, Abstract, Title, Keywords. RESULTS AND CONCLUSIONS: Highly cited documents were compared among Cortex, Neuropsychologia, and Brain, and a number of interesting parametric trends were observed. The characteristics of the papers that cite Cortex papers were examined, and some interesting insights were generated. Finally, the document clustering taxonomy showed that papers in Cortex can be reasonably divided into four categories (papers in each category in parenthesis): Semantic Memory (151); Handedness (145); Amnesia (119); and Neglect (66). It is concluded that Cortex needs to take steps to attract a more diverse group of contributors outside its continental Western European base if it wishes to capture a greater share of seminal neuropsychology papers. Further investigation of the critical citation differences reported in the paper is recommended.

Abstracting and Indexing↗
PubMed2004

Information content in Medline record fields.

BACKGROUND: The authors have been conducting text mining analyses (extraction of useful information from text) of Medline records, using Abstracts as the main data source. For literature-based discovery, and other text mining applications as well, all records in a discipline need to be evaluated for determining prior art. Many Medline records do not contain Abstracts, but typically contain Titles and Mesh terms. Substitution of these fields for Abstracts in the non-Abstract records would restore the missing literature to some degree. OBJECTIVES: Determine how well the information content of Title and Mesh fields approximates that of Abstracts in Medline records. APPROACH: Select historical Medline records related to Raynaud's Phenomenon that contain Abstracts. Determine the information content in the Abstract fields through text mining. Then, determine the information content in the Title fields, the Mesh fields, and the combined Title-Mesh fields, and compare with the information content in the Abstracts. RESULTS: Four metrics were used to compare the information content related to Raynaud's Phenomenon in the different fields: total number of phrases; number of unique phrases; content of factors from factor analyses; content of clusters from multi-link clustering. The Abstract field contains almost an order of magnitude more phrases than the other fields, and slightly more than an order of magnitude more unique phrases than the other fields. Each field used a factor matrix with 14 factors, and the combination of all 56 factors for the four fields represented 27 separate, but not unique, themes. These themes could be placed in two major categories, with two sub-categories per major category: Auto-immunity (antibodies, inflammation) and circulation (peripheral vessel circulation, coronary vessel circulation). All four sub-categories included representation from each field. Thus, while the focus of the representation of each field in each sub-category was moderately different, the four sub-category structure could be identified by analyzing the total factors in each field. In the cluster comparison phase of the study, the phrases used to create the clusters were the most important phrases identified for each factor. Thus, the factor matrix served as a filter for words used for clustering. While clusters were generated for all four fields, the Title hierarchy tended to be fragmented due to sparsity of the co-occurrence matrix that underlies the clusters. Therefore, the Title clusters were examined at only the lower levels of aggregation. The Abstract, Mesh, and Mesh + Title fields had the same first level taxonomy categories, auto-immunity and circulation. At the second level, the Abstract, Mesh, and Mesh + Title fields had the autoimmune diseases and antibodies sub-category in common. The Abstract and Mesh fields shared fascia inflammation as the other auto-immunity sub-category, while the other Mesh + Title sub-category focuses on vinyl chloride poisoning from industrial contact, and consequences of antineoplastic agents. However, in both cases, even though the words may be different, inflammation may be the common theme. CONCLUSIONS: For taxonomy generation, especially at the higher levels, each of the four fields has a similar thematic structure. At very detailed levels, the Mesh and Title fields run out of phrases relative to the Abstract field. Therefore, selection of field (s) to be employed for taxonomy generation depends on the objectives of the study, particularly the level of categorization required for the taxonomy. For information retrieval, or literature-based discovery, selection of the appropriate field again depends on the study objectives. If large queries, or large numbers of concepts or themes are desired, then the field with the largest number of technical phrases would be desirable. If queries or concepts represented by the more accepted popular terminology is adequate, then the smaller fields may be sufficient. Because of its established and controlled vocabulary, the Mesh field lags the Title or Abss the Title or Abstract fields in currency. Thus, the Title or Abstract fields would retrieve records with the most explicitly stated current concepts, but the Mesh field would capture a larger swath of fields that contained a concept of interest but perhaps had a wider range of specific terminology in the Abstract or Title text. In addition, this study provides the first validated estimate of the disparity in information retrieved through text mining limited to Titles and Mesh terms relative to entire Abstracts. As much of the older biomedical literature was entered into electronic databases without associated Abstracts, literature-based discovery exercises that search the older medical literature may miss a substantial proportion of relevant information. On the basis of this study, it may be estimated that up to a log order more information may be retrieved when complete Abstracts are searched.

Abstracting and Indexing↗
PubMed2004

Macromolecule mass spectrometry: citation mining of user documents.

Identifying research users, applications, and impact is important for research performers, managers, evaluators, and sponsors. Identification of the user audience and the research impact is complex and time consuming due to the many indirect pathways through which fundamental research can impact applications. This paper identified the literature pathways through which two highly-cited papers of 2002 Chemistry Nobel Laureates Fenn and Tanaka impacted research, technology development, and applications. Citation Mining, an integration of citation bibliometrics and text mining, was applied to the >1600 first generation Science Citation Index (SCI) citing papers to Fenn's 1989 Science paper on Electrospray Ionization for Mass Spectrometry, and to the >400 first generation SCI citing papers to Tanaka's 1988 Rapid Communications in Mass Spectrometry paper on Laser Ionization Time-of-Flight Mass Spectrometry. Bibliometrics was performed on the citing papers to profile the user characteristics. Text mining was performed on the citing papers to identify the technical areas impacted by the research, and the relationships among these technical areas.

Authorship↗