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Analysis of institutional authors

Arredondo, M. -T.AuthorFico, G.AuthorFioravanti, A.Author

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June 9, 2019
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Comparative assessment of glucose prediction models for patients with type 1 diabetes mellitus applying sensors for glucose and physical activity monitoring

Publicated to: MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING. 53 (12): 1333-1343 - 2015-12-01 53(12), DOI: 10.1007/s11517-015-1320-9

Authors:

Zarkogianni, K; Mitsis, K; Litsa, E; Arredondo, MT; Fico, G; Fioravanti, A; Nikita, KS
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Affiliations

Natl Tech Univ Athens, Biomed Simulat & Imaging Lab, Athens 15780, Greece - Author
Tech Univ Madrid, Madrid, Spain - Author

Abstract

The present work presents the comparative assessment of four glucose prediction models for patients with type 1 diabetes mellitus (T1DM) using data from sensors monitoring blood glucose concentration. The four models are based on a feedforward neural network (FNN), a self-organizing map (SOM), a neuro-fuzzy network with wavelets as activation functions (WFNN), and a linear regression model (LRM), respectively. For the development and evaluation of the models, data from 10 patients with T1DM for a 6-day observation period have been used. The models' predictive performance is evaluated considering a 30-, 60- and 120-min prediction horizon, using both mathematical and clinical criteria. Furthermore, the addition of input data from sensors monitoring physical activity is considered and its effect on the models' predictive performance is investigated. The continuous glucose-error grid analysis indicates that the models' predictive performance benefits mainly in the hypoglycemic range when additional information related to physical activity is fed into the models. The obtained results demonstrate the superiority of SOM over FNN, WFNN, and LRM with SOM leading to better predictive performance in terms of both mathematical and clinical evaluation criteria.
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Keywords

diabetesglucoseneural networksneuro-fuzzyphysical activitypredictionself-organizing mapAdultBlood glucoseDiabetesDiabetes mellitus, type 1FemaleGlucoseHumansIdentificationMaleMiddle agedModels, statisticalMonitoring, physiologicNeural networksNeural networks, computerNeuro-fuzzyPhysical activityPredictionSelf-organizing mapSensorsTime-series

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING due to its progression and the good impact it has achieved in recent years, according to the agency WoS (JCR), it has become a reference in its field. In the year of publication of the work, 2015, it was in position 11/20, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Medical Informatics. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Computer Science Applications.

From a relative perspective, and based on the normalized impact indicator calculated from World Citations provided by WoS (ESI, Clarivate), it yields a value for the citation normalization relative to the expected citation rate of: 2.32. This indicates that, compared to works in the same discipline and in the same year of publication, it ranks as a work cited above average. (source consulted: ESI Nov 13, 2025)

This information is reinforced by other indicators of the same type, which, although dynamic over time and dependent on the set of average global citations at the time of their calculation, consistently position the work at some point among the top 50% most cited in its field:

  • Weighted Average of Normalized Impact by the Scopus agency: 2.87 (source consulted: FECYT Mar 2025)

Specifically, and according to different indexing agencies, this work has accumulated citations as of 2026-04-27, the following number of citations:

  • WoS: 71
  • Scopus: 94
  • Europe PMC: 27
  • Google Scholar: 119
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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 2026-04-27:

  • 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: 122 (PlumX).

With a more dissemination-oriented intent and targeting more general audiences, we can observe other more global scores such as:

    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/87349/

    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: 188
    • Downloads: 40
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    Leadership analysis of institutional authors

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

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