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Analysis of institutional authors

Caraca-Valente Hernandez, Juan PedroAuthorAlonso, FCorresponding AuthorMartinez, LAuthorPerez, AAuthor

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January 29, 2014
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Generating reference models for structurally complex data. Application to the stabilometry medical domain

Publicated to: METHODS OF INFORMATION IN MEDICINE. 52 (5): 441-453 - 2013-10-31 52(5), DOI: 10.3414/ME12-01-0106

Authors:

Alonso, F; Lara, JA; Martinez, L; Pérez, A; Valente, JP
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Affiliations

Univ Distancia Madrid, Fac Ensenanzas Tecn, Madrid, Spain - Author
Univ Politecn Madrid, Fac Informat, CETTICO Res Grp, Dept Lenguajes & Sistemas Informat & Ingn Softwar, E-28660 Madrid, Spain - Author

Abstract

We present a framework specially designed to deal with structurally complex data, where all individuals have the same structure, as is the case in many medical domains. A structurally complex individual may be composed of any type of single-valued or multivalued attributes, including time series, for example. These attributes are structured according to domain-dependent hierarchies. Our aim is to generate reference models of population groups. These models represent the population archetype and are very useful for supporting such important tasks as diagnosis, detecting fraud, analyzing patient evolution, identifying control groups, etc.We have developed a conceptual model to represent structurally complex data hierarchically. Additionally, we have devised a method that uses the similarity tree concept to measure how similar two structurally complex individuals are, plus an outlier detection and filtering method. These methods provide the groundwork for the method that we have designed for generating reference models of a set of structurally complex individuals. A key idea of this method is to use event-based analysis for modeling time series.The proposed framework has been applied to the medical field of stabilometry. To validate the outlier detection method we used 142 individuals, and there was a match between the outlier ratings by the experts and by the system for 139 individuals (97.8%). To validate the reference model generation method, we applied k-fold cross validation (k = 5) with 60 athletes (basketball players and ice-skaters), and the system correctly classified 55 (91.7%). We then added 30 non-athletes as a control group, and the method output the correct result in a very high percentage of cases (96.6%).We have achieved very satisfactory results for the tests on data from such a complex domain as stabilometry and for the comparison of the reference model generation method with other methods. This supports the validity of this framework.
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Keywords

data miningoutlier detectionreference modelsstructurally complex dataAlgorithmAlgorithmsClassificationCluster analysisData miningHumansMedical informaticsModels, theoreticalOutlier detectionPatientsReference modelsReference standardsReproducibility of resultsSearchStructurally complex dataTime seriesTime-seriesTrends

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal METHODS OF INFORMATION IN MEDICINE due to its progression and the good impact it has achieved in recent years, according to the agency Scopus (SJR), it has become a reference in its field. In the year of publication of the work, 2013, it was in position , thus managing to position itself as a Q1 (Primer Cuartil), in the category Health Information Management.

Independientemente del impacto esperado determinado por el canal de difusión, es importante destacar el impacto real observado de la propia aportación.

Según las diferentes agencias de indexación, el número de citas acumuladas por esta publicación hasta la fecha 2026-04-24:

  • WoS: 7
  • Scopus: 8
  • Europe PMC: 4
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Impact and social visibility

From the perspective of influence or social adoption, and based on metrics associated with mentions and interactions provided by agencies specializing in calculating the so-called "Alternative or Social Metrics," we can highlight as of 2026-04-24:

  • The use of this contribution in bookmarks, code forks, additions to favorite lists for recurrent reading, as well as general views, indicates that someone is using the publication as a basis for their current work. This may be a notable indicator of future more formal and academic citations. This claim is supported by the result of the "Capture" indicator, which yields a total of: 27 (PlumX).

It is essential to present evidence supporting full alignment with institutional principles and guidelines on Open Science and the Conservation and Dissemination of Intellectual Heritage. A clear example of this is:

  • The work has been submitted to a journal whose editorial policy allows open Open Access publication.
  • Assignment of a Handle/URN as an identifier within the deposit in the Institutional Repository: https://oa.upm.es/26397/

As a result of the publication of the work in the institutional repository, statistical usage data has been obtained that reflects its impact. In terms of dissemination, we can state that, as of

  • Views: 350
  • Downloads: 310
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Leadership analysis of institutional authors

This work has been carried out with international collaboration, specifically with researchers from: Germany.

There is a significant leadership presence as some of the institution’s authors appear as the first or last signer, detailed as follows: First Author (ALONSO AMO, FERNANDO) and Last Author (Valente, JP).

the author responsible for correspondence tasks has been ALONSO AMO, FERNANDO.

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