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Analysis of institutional authors

Villa-Monedero, MaríaAuthorGil-Martin, ManuelAuthorSan-Segundo, RubenCorresponding Author

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January 19, 2025
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Transformer-Based Prediction of Hospital Readmissions for Diabetes Patients

Publicated to: Electronics. 14 (1): 174- - 2025-01-01 14(1), DOI: 10.3390/electronics14010174

Authors:

Garcia-Mosquera, Jorge; Villa-Monedero, Maria; Gil-Martin, Manuel; San-Segundo, Ruben
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Affiliations

Univ Politecn Madrid, Escuela Tecn Super Ingn Telecomunicac, Informat Proc & Telecommun Ctr, Speech Technol & Machine Learning Grp, Madrid 28040, Spain - Author

Abstract

Artificial intelligence is having a strong impact on healthcare services, improving their quality and efficiency. This paper proposes and evaluates a prediction system of hospital readmissions for diabetes patients. This system is based on a Transformer, a state-of-the-art deep learning architecture integrating different types of information and features in the same model. This architecture integrates several attention heads to model the contribution of each feature to the global prediction. The main target of this work is to provide a decision support tool to help manage hospital resources effectively. This system was developed and evaluated using the United States Health Facts Database, which includes information and features from 101,766 diabetes patients between 1999 and 2008. The experiments were conducted using a patient-wise cross-validation strategy, ensuring that the patients used to develop the system were not used in the final test. These experiments demonstrated the Transformer's strong ability to combine different features, providing slightly better results compared to previous results reported on this dataset. These experiments allow us to report the prediction accuracy for multiple class numbers. Finally, this paper provides a detailed analysis of the relevance of each feature when predicting hospital readmissions.
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Keywords

Combination of different types of featureCombination of different types of featuresDiabetes patientsDischargFeature analysisHospital readmission predictionManagementTransformer-based prediction

Quality index

Bibliometric impact. Analysis of the contribution and dissemination channel

The work has been published in the journal Electronics 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, 2025, it was in position 174/368, thus managing to position itself as a Q2 (Segundo Cuartil), in the category Engineering, Electrical & Electronic. Notably, the journal is positioned en el Cuartil Q2 para la agencia Scopus (SJR) en la categoría Electrical and Electronic Engineering.

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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 2026-04-25:

  • 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: 26 (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/85728/

    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: 190
    • Downloads: 208
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    Leadership analysis of institutional authors

    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 (García-Mosquera, Jorge) and Last Author (SAN SEGUNDO HERNANDEZ, RUBEN).

    the author responsible for correspondence tasks has been SAN SEGUNDO HERNANDEZ, RUBEN.

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    Awards linked to the item

    This work was funded by Project ASTOUND (101071191-HORIZONEIC-2021-PATHFINDERCHALLENGES-01) of the European Commission and by the Spanish Ministry of Science and Innovation through the projects GOMINOLA (PID2020-118112RB-C22), BeWord (PID2021-126061OB-C43) and TRUSTBOOST (PID2023-150584OB-C21), funded by MCIN/AEI/10.13039/501100011033 and by the European Union "NextGenerationEU/PRTR".
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