Publicaciones científicas
2025
Bayón-Gutiérrez, Martín; Prieto-Fernández, Natalia; García-Ordás, María Teresa; Benítez-Andrades, José Alberto; Alaiz-Moretón, Héctor; Grisetti, Giorgio
CAD2SLAM: Adaptive Projection Between CAD Blueprints and SLAM Maps Artículo de revista
En: IEEE Robotics and Automation Letters, vol. 10, no 2, pp. 1529–1536, 2025, ISSN: 2377-3766.
@article{bayon-gutierrez_cad2slam_2025,
title = {CAD2SLAM: Adaptive Projection Between CAD Blueprints and SLAM Maps},
author = {Martín Bayón-Gutiérrez and Natalia Prieto-Fernández and María Teresa García-Ordás and José Alberto Benítez-Andrades and Héctor Alaiz-Moretón and Giorgio Grisetti},
url = {https://ieeexplore.ieee.org/document/10816387},
doi = {10.1109/LRA.2024.3522838},
issn = {2377-3766},
year = {2025},
date = {2025-02-01},
urldate = {2025-08-30},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {2},
pages = {1529–1536},
abstract = {Robotic mobile platforms are key building blocks for numerous applications and cooperation between robots and humans is a key aspect to enhance productivity and reduce labor cost. To operate safely, robots typically rely on a custom map of the environment that depends on the sensor configuration of the platform. In contrast, blueprints stand as an abstract representation of the environment. The use of both CAD and SLAM maps allows robots to collaborate using the blueprint as a common language, while also easing the control for non-robotics experts. In this work we present an adaptive system to project a 2D pose in the blueprint to the SLAM map and vice-versa. Previous work in the literature aims at morphing a SLAM map to a previously available map. In contrast, CAD2SLAM does not alter the internal map representation used by the SLAM and localization algorithms running on the robot, preserving its original properties. We believe that our system is beneficial for the control and supervision of multiple heterogeneous robotic platforms that are monitored and controlled through the CAD map. Finally, we present a set of experiments that support our claims as well as open-source implementation.},
keywords = {},
pubstate = {published},
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}
Robotic mobile platforms are key building blocks for numerous applications and cooperation between robots and humans is a key aspect to enhance productivity and reduce labor cost. To operate safely, robots typically rely on a custom map of the environment that depends on the sensor configuration of the platform. In contrast, blueprints stand as an abstract representation of the environment. The use of both CAD and SLAM maps allows robots to collaborate using the blueprint as a common language, while also easing the control for non-robotics experts. In this work we present an adaptive system to project a 2D pose in the blueprint to the SLAM map and vice-versa. Previous work in the literature aims at morphing a SLAM map to a previously available map. In contrast, CAD2SLAM does not alter the internal map representation used by the SLAM and localization algorithms running on the robot, preserving its original properties. We believe that our system is beneficial for the control and supervision of multiple heterogeneous robotic platforms that are monitored and controlled through the CAD map. Finally, we present a set of experiments that support our claims as well as open-source implementation.
Pastor-Vargas, Rafael; Antón-Munárriz, Cristina; Haut, Juan M.; Robles-Gómez, Antonio; Paoletti, Mercedes E.; Benítez-Andrades, José Alberto
Cerebral ischemia detection using deep learning techniques Artículo de revista
En: Health Information Science and Systems, vol. 13, no 1, pp. 36, 2025, ISSN: 2047-2501.
@article{pastor-vargas_cerebral_2025,
title = {Cerebral ischemia detection using deep learning techniques},
author = {Rafael Pastor-Vargas and Cristina Antón-Munárriz and Juan M. Haut and Antonio Robles-Gómez and Mercedes E. Paoletti and José Alberto Benítez-Andrades},
url = {https://doi.org/10.1007/s13755-025-00352-8},
doi = {10.1007/s13755-025-00352-8},
issn = {2047-2501},
year = {2025},
date = {2025-05-01},
urldate = {2025-08-30},
journal = {Health Information Science and Systems},
volume = {13},
number = {1},
pages = {36},
abstract = {Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.},
keywords = {},
pubstate = {published},
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Cerebrovascular accident (CVA), commonly known as stroke, stands as a significant contributor to contemporary mortality and morbidity rates, often leading to lasting disabilities. Early identification is crucial in mitigating its impact and reducing mortality. Non-contrast computed tomography (NCCT) remains the primary diagnostic tool in stroke emergencies due to its speed, accessibility, and cost-effectiveness. NCCT enables the exclusion of hemorrhage and directs attention to ischemic causes resulting from arterial flow obstruction. Quantification of NCCT findings employs the Alberta Stroke Program Early Computed Tomography Score (ASPECTS), which evaluates affected brain structures. This study seeks to identify early alterations in NCCT density in patients with stroke symptoms using a binary classifier distinguishing NCCT scans with and without stroke. To achieve this, various well-known deep learning architectures, namely VGG3D, ResNet3D, and DenseNet3D, validated in the ImageNet challenges, are implemented with 3D images covering the entire brain volume. The training results of these networks are presented, wherein diverse parameters are examined for optimal performance. The DenseNet3D network emerges as the most effective model, attaining a training set accuracy of 98% and a test set accuracy of 95%. The aim is to alert medical professionals to potential stroke cases in their early stages based on NCCT findings displaying altered density patterns.
Albert, Elvira; Hähnle, Reiner; Merayo, Alicia; Steinhöfel, Dominic
Certified Cost Bounds for Abstract Programs Artículo de revista
En: ACM Trans. Softw. Eng. Methodol., vol. 34, no 3, pp. 67:1–67:33, 2025, ISSN: 1049-331X.
@article{albert_certified_2025,
title = {Certified Cost Bounds for Abstract Programs},
author = {Elvira Albert and Reiner Hähnle and Alicia Merayo and Dominic Steinhöfel},
url = {https://dl.acm.org/doi/10.1145/3705298},
doi = {10.1145/3705298},
issn = {1049-331X},
year = {2025},
date = {2025-02-01},
urldate = {2025-08-30},
journal = {ACM Trans. Softw. Eng. Methodol.},
volume = {34},
number = {3},
pages = {67:1–67:33},
abstract = {A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation or optimization. Static cost analysis derives the precise cost—or upper and lower bounds for it—of executing programs, as functions in terms of the program's input data size. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the cost effect of program transformations. This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
A program containing placeholders for unspecified statements or expressions is called an abstract (or schematic) program. Placeholder symbols occur naturally in program transformation rules, as used in refactoring, compilation or optimization. Static cost analysis derives the precise cost—or upper and lower bounds for it—of executing programs, as functions in terms of the program's input data size. We present a generalization of automated cost analysis that can handle abstract programs and, hence, can analyze the impact on the cost effect of program transformations. This kind of relational property requires provably precise cost bounds which are not always produced by cost analysis. Therefore, we certify by deductive verification that the inferred abstract cost bounds are correct and sufficiently precise. It is the first approach solving this problem. Both, abstract cost analysis and certification, are based on quantitative abstract execution (QAE) which in turn is a variation of abstract execution, a recently developed symbolic execution technique for abstract programs. To realize QAE the new concept of a cost invariant is introduced. QAE is implemented and runs fully automatically on a benchmark set consisting of representative optimization rules.
Nforh, Lawrence; Michelena, Álvaro; Aveleira-Mata, Jose; García-Ordás, María Teresa; Zayas-Gato, Francisco; Jove, Esteban; Alaiz-Moretón, Héctor
DoS Attack Detection and Identification over Zigbee Environments Using Supervised Classification Algorithms Proceedings Article
En: Novais, Paulo; D., Parameshachari B.; Satoh, Ichiro; Inglada, Vicente Julian; González, Sara Rodríguez; Pérez, Esteban Jove; Domínguez, Javier Parra; Chamoso, Pablo; Alonso, Ricardo S. (Ed.): Ambient Intelligence – Software and Applications – 15th International Symposium on Ambient Intelligence, pp. 337–347, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-83117-1.
@inproceedings{nforh_dos_2025,
title = {DoS Attack Detection and Identification over Zigbee Environments Using Supervised Classification Algorithms},
author = {Lawrence Nforh and Álvaro Michelena and Jose Aveleira-Mata and María Teresa García-Ordás and Francisco Zayas-Gato and Esteban Jove and Héctor Alaiz-Moretón},
editor = {Paulo Novais and Parameshachari B. D. and Ichiro Satoh and Vicente Julian Inglada and Sara Rodríguez González and Esteban Jove Pérez and Javier Parra Domínguez and Pablo Chamoso and Ricardo S. Alonso},
doi = {10.1007/978-3-031-83117-1_32},
isbn = {978-3-031-83117-1},
year = {2025},
date = {2025-01-01},
booktitle = {Ambient Intelligence – Software and Applications – 15th International Symposium on Ambient Intelligence},
pages = {337–347},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The Zigbee protocol, designed for low-power personal area wireless networks, is a technology widely used on the Internet of Things. This paper presents a study on the detection of denial-of-service attacks in Zigbee networks using supervised classification algorithms. Three techniques are evaluated: Logistic Regression, K-Nearest Neighbors and Support Vector Machines. A generated dataset is used for the analysis, and the results show that the K-Nearest Neighbors and Support Vector Machines approach achieves high performance and low computational demand. This methodology offers a promising strategy for security in Zigbee networks.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The Zigbee protocol, designed for low-power personal area wireless networks, is a technology widely used on the Internet of Things. This paper presents a study on the detection of denial-of-service attacks in Zigbee networks using supervised classification algorithms. Three techniques are evaluated: Logistic Regression, K-Nearest Neighbors and Support Vector Machines. A generated dataset is used for the analysis, and the results show that the K-Nearest Neighbors and Support Vector Machines approach achieves high performance and low computational demand. This methodology offers a promising strategy for security in Zigbee networks.
Benítez-Andrades, José Alberto; Prada-García, Camino; Ordás-Reyes, Nicolás; Blanco, Marta Esteban; Merayo, Alicia; Serrano-García, Antonio
Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods Artículo de revista
En: Health Information Science and Systems, vol. 13, no 1, pp. 24, 2025, ISSN: 2047-2501.
@article{benitez-andrades_enhanced_2025,
title = {Enhanced prediction of spine surgery outcomes using advanced machine learning techniques and oversampling methods},
author = {José Alberto Benítez-Andrades and Camino Prada-García and Nicolás Ordás-Reyes and Marta Esteban Blanco and Alicia Merayo and Antonio Serrano-García},
url = {https://doi.org/10.1007/s13755-025-00343-9},
doi = {10.1007/s13755-025-00343-9},
issn = {2047-2501},
year = {2025},
date = {2025-03-01},
urldate = {2025-08-30},
journal = {Health Information Science and Systems},
volume = {13},
number = {1},
pages = {24},
abstract = {Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Accurate prediction of spine surgery outcomes is essential for optimizing treatment strategies. This study presents an enhanced machine learning approach to classify and predict the success of spine surgeries, incorporating advanced oversampling techniques and grid search optimization to improve model performance.
González-Castro, Ana; Benítez-Andrades, José Alberto; González-González, Rubén; Prada-García, Camino; Leirós-Rodríguez, Raquel
Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors Artículo de revista
En: DIGITAL HEALTH, vol. 11, pp. 20552076251331752, 2025, ISSN: 2055-2076, (Publisher: SAGE Publications Ltd).
@article{gonzalez-castro_predicting_2025,
title = {Predicting fall risk in older adults: A machine learning comparison of accelerometric and non-accelerometric factors},
author = {Ana González-Castro and José Alberto Benítez-Andrades and Rubén González-González and Camino Prada-García and Raquel Leirós-Rodríguez},
url = {https://doi.org/10.1177/20552076251331752},
doi = {10.1177/20552076251331752},
issn = {2055-2076},
year = {2025},
date = {2025-05-01},
urldate = {2025-08-30},
journal = {DIGITAL HEALTH},
volume = {11},
pages = {20552076251331752},
abstract = {ObjectivesAccurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, aiming to improve predictive accuracy and identify key contributing variable.MethodsWe applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Performance was evaluated based on mean squared error (MSE) and coefficient of determination (R2), to assess how combining multiple data types influences prediction accuracy.ResultsModels trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746},
note = {Publisher: SAGE Publications Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ObjectivesAccurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, aiming to improve predictive accuracy and identify key contributing variable.MethodsWe applied random forest, XGBoost, AdaBoost, LightGBM, support vector regression (SVR), decision trees, and Bayesian ridge regression to a dataset of 146 older adults. Models were trained using accelerometric data (movement patterns) and non-accelerometric data (demographic and clinical variables). Performance was evaluated based on mean squared error (MSE) and coefficient of determination (R2), to assess how combining multiple data types influences prediction accuracy.ResultsModels trained on combined accelerometric and non-accelerometric data consistently outperformed those based on single data types. Bayesian ridge regression achieved the highest accuracy (MSE = 0.6746
Martínez-Villamea, Silvia; Prada-García, Camino; Benítez-Andrades, José Alberto; Quiroga-Sánchez, Enedina; García-Fernández, Rubén; Arias-Ramos, Natalia
Sleep Disturbances and Dietary Habits in Autism: A Comparative Analysis Artículo de revista
En: Journal of Autism and Developmental Disorders, 2025, ISSN: 1573-3432.
@article{martinez-villamea_sleep_2025,
title = {Sleep Disturbances and Dietary Habits in Autism: A Comparative Analysis},
author = {Silvia Martínez-Villamea and Camino Prada-García and José Alberto Benítez-Andrades and Enedina Quiroga-Sánchez and Rubén García-Fernández and Natalia Arias-Ramos},
url = {https://doi.org/10.1007/s10803-025-06964-z},
doi = {10.1007/s10803-025-06964-z},
issn = {1573-3432},
year = {2025},
date = {2025-07-01},
urldate = {2025-08-30},
journal = {Journal of Autism and Developmental Disorders},
abstract = {This study investigates dietary patterns and sleep quality in children and adolescents on the autism spectrum, compared to non-autistic peers. It explores the relationship between dietary habits and sleep quality, aiming to identify modifiable factors that could enhance well-being in ASD individuals. A cross-sectional case–control study was conducted with 158 participants on the autism spectrum and 77 non-autistic individuals aged 6–17 years in Spain. Dietary patterns were assessed using a validated food frequency questionnaire, while sleep quality was measured with the Children’s Sleep Habits Questionnaire (CSHQ-SP) and Pittsburgh Sleep Quality Index (PSQI). Statistical analyses, including non-parametric tests and Spearman’s correlation, were performed to examine differences and associations. Children on the autism spectrum displayed higher sugar intake and lower consumption of fruits and vegetables compared to non-autistic peers. ASD adolescents consumed more sugary beverages, with less pronounced differences in other food categories. Sleep quality was significantly poorer in the ASD group across all age cohorts, characterized by increased sleep latency, parasomnias, and daytime dysfunction. Positive associations were found between fruit and vegetable intake and improved sleep quality in ASD children. Unexpectedly, adolescents on the autism spectrum showed a complex relationship between sugar consumption and sleep quality, indicating potential short-term benefits but long-term risks. This study highlights the interplay between diet and sleep quality in ASD populations. Interventions promoting healthier eating habits, such as increased fruit and vegetable intake and reduced sugar consumption, could improve sleep outcomes and overall well-being in this vulnerable population.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
This study investigates dietary patterns and sleep quality in children and adolescents on the autism spectrum, compared to non-autistic peers. It explores the relationship between dietary habits and sleep quality, aiming to identify modifiable factors that could enhance well-being in ASD individuals. A cross-sectional case–control study was conducted with 158 participants on the autism spectrum and 77 non-autistic individuals aged 6–17 years in Spain. Dietary patterns were assessed using a validated food frequency questionnaire, while sleep quality was measured with the Children’s Sleep Habits Questionnaire (CSHQ-SP) and Pittsburgh Sleep Quality Index (PSQI). Statistical analyses, including non-parametric tests and Spearman’s correlation, were performed to examine differences and associations. Children on the autism spectrum displayed higher sugar intake and lower consumption of fruits and vegetables compared to non-autistic peers. ASD adolescents consumed more sugary beverages, with less pronounced differences in other food categories. Sleep quality was significantly poorer in the ASD group across all age cohorts, characterized by increased sleep latency, parasomnias, and daytime dysfunction. Positive associations were found between fruit and vegetable intake and improved sleep quality in ASD children. Unexpectedly, adolescents on the autism spectrum showed a complex relationship between sugar consumption and sleep quality, indicating potential short-term benefits but long-term risks. This study highlights the interplay between diet and sleep quality in ASD populations. Interventions promoting healthier eating habits, such as increased fruit and vegetable intake and reduced sugar consumption, could improve sleep outcomes and overall well-being in this vulnerable population.
Rubio-Martín, Sergio; García-Ordás, María Teresa; Corral-Fontecha, David; López-González, Laura; Alonso-Oláiz, Gonzalo; Crespo-Álvaro, Arturo; Benítez-Andrades, José Alberto
AI-Driven Survival Prediction in Pancreatic Cancer Proceedings Article
En: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), pp. 284–289, 2025, (ISSN: 2372-9198).
@inproceedings{rubio-martin_ai-driven_2025,
title = {AI-Driven Survival Prediction in Pancreatic Cancer},
author = {Sergio Rubio-Martín and María Teresa García-Ordás and David Corral-Fontecha and Laura López-González and Gonzalo Alonso-Oláiz and Arturo Crespo-Álvaro and José Alberto Benítez-Andrades},
url = {https://ieeexplore.ieee.org/document/11058753},
doi = {10.1109/CBMS65348.2025.00064},
year = {2025},
date = {2025-06-01},
urldate = {2025-06-01},
booktitle = {2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {284–289},
abstract = {Pancreatic cancer remains one of the most aggressive malignancies, with limited survival rates and significant variability in patient outcomes. This study evaluates the performance of three machine learning models (Random Forest, Decision Tree, and XGBoost) in predicting patient survival at 3, 12, and 18 months, using data from the Complejo Asistencial Universitario de León (CAULE) Radiology Department. To systematically analyze the impact of different features on survival prediction, the dataset was structured into seven variable groups (G1G7), incorporating demographic, clinical, and treatment-related information. To address the inherent class imbalance in survival prediction, an Autoencoder-based synthetic data generation approach was applied, ensuring a balanced distribution of survival and non-survival cases across all timeframes. Hyperparameter tuning was performed, and experimental results indicate that Random Forest and XGBoost achieved comparable performance, both obtaining an accuracy above 81 % at 3 months, 83 % at 12 months, and 88 % at 18 months when trained on Group G7. To enhance model interpretability, SHapley Additive exPlanations (SHAP) was applied to the best-performing model, identifying key factors influencing survival.},
note = {ISSN: 2372-9198},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Pancreatic cancer remains one of the most aggressive malignancies, with limited survival rates and significant variability in patient outcomes. This study evaluates the performance of three machine learning models (Random Forest, Decision Tree, and XGBoost) in predicting patient survival at 3, 12, and 18 months, using data from the Complejo Asistencial Universitario de León (CAULE) Radiology Department. To systematically analyze the impact of different features on survival prediction, the dataset was structured into seven variable groups (G1G7), incorporating demographic, clinical, and treatment-related information. To address the inherent class imbalance in survival prediction, an Autoencoder-based synthetic data generation approach was applied, ensuring a balanced distribution of survival and non-survival cases across all timeframes. Hyperparameter tuning was performed, and experimental results indicate that Random Forest and XGBoost achieved comparable performance, both obtaining an accuracy above 81 % at 3 months, 83 % at 12 months, and 88 % at 18 months when trained on Group G7. To enhance model interpretability, SHapley Additive exPlanations (SHAP) was applied to the best-performing model, identifying key factors influencing survival.
Fontecha, David Corral; Fernández-Miranda, Pablo Menendez; Rubio-Martín, Sergio; Merayo-Corcoba, Alicia; González, Laura Lopez; Iglesias, Lara Lloret; Vega, Jose A
Enhancing Radiomic Feature Robustness Through Voxel Spacing-Aware Extraction in Anisotropic CT Data Proceedings Article
En: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), pp. 490–495, 2025, (ISSN: 2372-9198).
@inproceedings{fontecha_enhancing_2025,
title = {Enhancing Radiomic Feature Robustness Through Voxel Spacing-Aware Extraction in Anisotropic CT Data},
author = {David Corral Fontecha and Pablo Menendez Fernández-Miranda and Sergio Rubio-Martín and Alicia Merayo-Corcoba and Laura Lopez González and Lara Lloret Iglesias and Jose A Vega},
url = {https://ieeexplore.ieee.org/document/11058871},
doi = {10.1109/CBMS65348.2025.00103},
year = {2025},
date = {2025-06-01},
urldate = {2025-08-30},
booktitle = {2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {490–495},
abstract = {This study aimed to evaluate whether voxel spacing-aware radiomic feature extraction improves reproducibility, variability, and discriminative performance compared to conventional preprocessing methods in anisotropic CT data. A curated cohort of 685 pulmonary nodules from the LIDC-IDRI dataset was analyzed. Three preprocessing strategies-no resampling, isotropic resampling, and voxel spacing-aware extraction-and one postprocessing approach, voxel spacing weighting, were systematically compared. A modified version of PyRadiomics was developed to compute texture and shape features directly from native images while incorporating physical voxel dimensions without interpolation. Among the 94 extracted features, spacingaware preprocessing improved reproducibility in 58 features, reduced variability in 48, and enhanced univariate discrimination in 37. An ensemble feature selection combining six statistical and machine learning methods identified between 18 and 20 robust features per method. Logistic Regression models trained with spacing-aware features achieved the highest composite performance score (1.498), balancing discrimination and generalizability. SHAP interpretability analysis confirmed the clinical relevance of selected geometric and texture features. Overall, voxel spacing-aware preprocessing preserved native spatial structure and mitigated the effects of voxel geometry and interpolation artifacts, yielding stable and clinically interpretable radiomic features. These findings support the adoption of spacing-aware pipelines in heterogeneous and multi-center CT radiomics studies to enhance feature robustness and reproducibility.},
note = {ISSN: 2372-9198},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This study aimed to evaluate whether voxel spacing-aware radiomic feature extraction improves reproducibility, variability, and discriminative performance compared to conventional preprocessing methods in anisotropic CT data. A curated cohort of 685 pulmonary nodules from the LIDC-IDRI dataset was analyzed. Three preprocessing strategies-no resampling, isotropic resampling, and voxel spacing-aware extraction-and one postprocessing approach, voxel spacing weighting, were systematically compared. A modified version of PyRadiomics was developed to compute texture and shape features directly from native images while incorporating physical voxel dimensions without interpolation. Among the 94 extracted features, spacingaware preprocessing improved reproducibility in 58 features, reduced variability in 48, and enhanced univariate discrimination in 37. An ensemble feature selection combining six statistical and machine learning methods identified between 18 and 20 robust features per method. Logistic Regression models trained with spacing-aware features achieved the highest composite performance score (1.498), balancing discrimination and generalizability. SHAP interpretability analysis confirmed the clinical relevance of selected geometric and texture features. Overall, voxel spacing-aware preprocessing preserved native spatial structure and mitigated the effects of voxel geometry and interpolation artifacts, yielding stable and clinically interpretable radiomic features. These findings support the adoption of spacing-aware pipelines in heterogeneous and multi-center CT radiomics studies to enhance feature robustness and reproducibility.
García-Ordás, María Teresa; Arcano-Bea, Paula; Rubiños, Manuel; Jove, Esteban; Narciandi-Rodriguez, Diego; Alaiz-Moretón, Héctor
Feature Importance Analysis of Meteorological Weather for Mini Eolic Electrical Power Prediction Using Clustering Information Proceedings Article
En: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Jove, Esteban; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Cosío, Álvaro Herrero; Fosci, Paolo (Ed.): The 19th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2024, pp. 293–303, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-75010-6.
@inproceedings{garcia-ordas_feature_2025,
title = {Feature Importance Analysis of Meteorological Weather for Mini Eolic Electrical Power Prediction Using Clustering Information},
author = {María Teresa García-Ordás and Paula Arcano-Bea and Manuel Rubiños and Esteban Jove and Diego Narciandi-Rodriguez and Héctor Alaiz-Moretón},
editor = {Héctor Quintián and Emilio Corchado and Alicia Troncoso Lora and Hilde Pérez García and Esteban Jove and José Luis Calvo Rolle and Francisco Javier Martínez de Pisón and Pablo García Bringas and Francisco Martínez Álvarez and Álvaro Herrero Cosío and Paolo Fosci},
doi = {10.1007/978-3-031-75010-6_29},
isbn = {978-3-031-75010-6},
year = {2025},
date = {2025-01-01},
booktitle = {The 19th International Conference on Soft Computing Models in Industrial and Environmental Applications SOCO 2024},
pages = {293–303},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The increasing concern about climate change has contributed to promoting renewable energy technologies. Mini-eolic turbines are a common solution for domestic energy supply self-consumption installations. Due to this technology’s strong dependency on climate conditions and corresponding variability, it is important to develop intelligent systems to model and estimate its behavior. This paper uses three different feature selection methods, a clustering algorithm, and a regression technique to predict the power generated by a small wind turbine located in a bioclimatic house. Different configurations are tested, evaluating the impact on the model performance.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The increasing concern about climate change has contributed to promoting renewable energy technologies. Mini-eolic turbines are a common solution for domestic energy supply self-consumption installations. Due to this technology’s strong dependency on climate conditions and corresponding variability, it is important to develop intelligent systems to model and estimate its behavior. This paper uses three different feature selection methods, a clustering algorithm, and a regression technique to predict the power generated by a small wind turbine located in a bioclimatic house. Different configurations are tested, evaluating the impact on the model performance.
Rubio-Martín, Sergio; Crespo-Álvaro, Arturo; García-Ordás, María Teresa; Serrano-García, Antonio; Franch-Pato, Clara Margarita; Benítez-Andrades, José Alberto
Fine-Tuning Transformer Models for Structuring Spanish Psychiatric Clinical Notes Proceedings Article
En: 2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS), pp. 97–102, 2025, (ISSN: 2372-9198).
@inproceedings{rubio-martin_fine-tuning_2025,
title = {Fine-Tuning Transformer Models for Structuring Spanish Psychiatric Clinical Notes},
author = {Sergio Rubio-Martín and Arturo Crespo-Álvaro and María Teresa García-Ordás and Antonio Serrano-García and Clara Margarita Franch-Pato and José Alberto Benítez-Andrades},
url = {https://ieeexplore.ieee.org/document/11058869},
doi = {10.1109/CBMS65348.2025.00028},
year = {2025},
date = {2025-06-01},
urldate = {2025-08-30},
booktitle = {2025 IEEE 38th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {97–102},
abstract = {The unstructured nature of psychiatric clinical notes poses a significant challenge for automated information extraction and data structuring. In this study, we explore the use of transformer-based language models to perform Named Entity Recognition (NER) on de-identified Spanish electronic health records (EHRs) provided by the Psychiatry Service of Complejo Asistencial Universitario de León (CAULE). A manually annotated gold standard, consisting of 200 clinical notes, was developed by domain experts to evaluate the performance of five models: BETO (cased and uncased), ALBETO, ClinicalBERT, and Bio_ClinicalBERT. Each model was fine-tuned and assessed using a strict exact matching criterion across six clinically relevant label types. Results demonstrate that ClinicalBERT, despite being pre-trained on English medical corpora, achieved the highest macro-average F1-score on the test set (80 %). However, BETO-cased outperformed ClinicalBERT in four out of six label types, being better in categories with higher syntactic variability. Lower-performing models, such as ALBETO and Bio_ClinicalBERT, struggled to generalize to Spanish psychiatric language, likely due to domain and language mismatches. This work highlights the effectiveness of transformer-based architectures for structuring psychiatric narratives in Spanish and provides a robust foundation for future clinical NLP applications in non-English contexts.},
note = {ISSN: 2372-9198},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The unstructured nature of psychiatric clinical notes poses a significant challenge for automated information extraction and data structuring. In this study, we explore the use of transformer-based language models to perform Named Entity Recognition (NER) on de-identified Spanish electronic health records (EHRs) provided by the Psychiatry Service of Complejo Asistencial Universitario de León (CAULE). A manually annotated gold standard, consisting of 200 clinical notes, was developed by domain experts to evaluate the performance of five models: BETO (cased and uncased), ALBETO, ClinicalBERT, and Bio_ClinicalBERT. Each model was fine-tuned and assessed using a strict exact matching criterion across six clinically relevant label types. Results demonstrate that ClinicalBERT, despite being pre-trained on English medical corpora, achieved the highest macro-average F1-score on the test set (80 %). However, BETO-cased outperformed ClinicalBERT in four out of six label types, being better in categories with higher syntactic variability. Lower-performing models, such as ALBETO and Bio_ClinicalBERT, struggled to generalize to Spanish psychiatric language, likely due to domain and language mismatches. This work highlights the effectiveness of transformer-based architectures for structuring psychiatric narratives in Spanish and provides a robust foundation for future clinical NLP applications in non-English contexts.
García-Ordás, María Teresa; Díaz-Longueira, Antonio; Michelena, Álvaro; Jove, Esteban; Bayón-Gutiérrez, Martín; Alaiz-Moretón, Héctor
Missing Meteorological Data Imputation for Mini Eolic Electrical Power Prediction Proceedings Article
En: Quintián, Héctor; Corchado, Emilio; Lora, Alicia Troncoso; García, Hilde Pérez; Pérez, Esteban Jove; Rolle, José Luis Calvo; de Pisón, Francisco Javier Martínez; Bringas, Pablo García; Álvarez, Francisco Martínez; Herrero, Álvaro; Fosci, Paolo (Ed.): Hybrid Artificial Intelligent Systems, pp. 78–87, Springer Nature Switzerland, Cham, 2025, ISBN: 978-3-031-74186-9.
@inproceedings{garcia-ordas_missing_2025,
title = {Missing Meteorological Data Imputation for Mini Eolic Electrical Power Prediction},
author = {María Teresa García-Ordás and Antonio Díaz-Longueira and Álvaro Michelena and Esteban Jove and Martín Bayón-Gutiérrez and Héctor Alaiz-Moretón},
editor = {Héctor Quintián and Emilio Corchado and Alicia Troncoso Lora and Hilde Pérez García and Esteban Jove Pérez and José Luis Calvo Rolle and Francisco Javier Martínez de Pisón and Pablo García Bringas and Francisco Martínez Álvarez and Álvaro Herrero and Paolo Fosci},
doi = {10.1007/978-3-031-74186-9_7},
isbn = {978-3-031-74186-9},
year = {2025},
date = {2025-01-01},
booktitle = {Hybrid Artificial Intelligent Systems},
pages = {78–87},
publisher = {Springer Nature Switzerland},
address = {Cham},
abstract = {The continuous rising trend shown by greenhouse emissions has led to a global situation in which the promotion of clean alternative technologies is crucial. In this context, small green power self-consumption installations represent an effective and clean solution to reduce climate change. However, they must be subjected to exhaustive supervision of the process, from mechanical, electrical, or electronic components, to ensure good performance and economic feasibility. This work proposes different data imputation techniques to deal with missing data derived from sensor missreadings in a minieolic installation. The performance of regression techniques over each reconstructed set is evaluated with successful results.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
The continuous rising trend shown by greenhouse emissions has led to a global situation in which the promotion of clean alternative technologies is crucial. In this context, small green power self-consumption installations represent an effective and clean solution to reduce climate change. However, they must be subjected to exhaustive supervision of the process, from mechanical, electrical, or electronic components, to ensure good performance and economic feasibility. This work proposes different data imputation techniques to deal with missing data derived from sensor missreadings in a minieolic installation. The performance of regression techniques over each reconstructed set is evaluated with successful results.
Rubio-Martín, Sergio; García-Ordás, María Teresa; Serrano-García, Antonio; Franch-Pato, Clara Margarita; Crespo-Álvaro, Arturo; Benítez-Andrades, José Alberto
Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach Artículo de revista
En: PeerJ Computer Science, vol. 11, pp. e3045, 2025, ISSN: 2376-5992, (Publisher: PeerJ Inc.).
@article{rubio-martin_classification_2025,
title = {Classification of psychiatry clinical notes by diagnosis: a deep learning and machine learning approach},
author = {Sergio Rubio-Martín and María Teresa García-Ordás and Antonio Serrano-García and Clara Margarita Franch-Pato and Arturo Crespo-Álvaro and José Alberto Benítez-Andrades},
url = {https://peerj.com/articles/cs-3045},
doi = {10.7717/peerj-cs.3045},
issn = {2376-5992},
year = {2025},
date = {2025-07-01},
urldate = {2025-08-30},
journal = {PeerJ Computer Science},
volume = {11},
pages = {e3045},
abstract = {The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like anxiety and adjustment disorder. In this study, we compare the performance of various artificial intelligence models, including both traditional machine learning approaches (random forest, support vector machine, K-nearest neighbors, decision tree, and eXtreme Gradient Boost) and deep learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Over-sampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with Bidirectional Encoder Representations from Transformers (BERT)-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The decision tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.},
note = {Publisher: PeerJ Inc.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The classification of clinical notes into specific diagnostic categories is critical in healthcare, especially for mental health conditions like anxiety and adjustment disorder. In this study, we compare the performance of various artificial intelligence models, including both traditional machine learning approaches (random forest, support vector machine, K-nearest neighbors, decision tree, and eXtreme Gradient Boost) and deep learning models (DistilBERT and SciBERT), to classify clinical notes into these two diagnoses. Additionally, we implemented three oversampling strategies: No Oversampling, Random Oversampling, and Synthetic Minority Over-sampling Technique (SMOTE), to assess their impact on model performance. Hyperparameter tuning was also applied to optimize model accuracy. Our results indicate that oversampling techniques had minimal impact on model performance overall. The only exception was SMOTE, which showed a positive effect specifically with Bidirectional Encoder Representations from Transformers (BERT)-based models. However, hyperparameter optimization significantly improved accuracy across the models, enhancing their ability to generalize and perform on the dataset. The decision tree and eXtreme Gradient Boost models achieved the highest accuracy among machine learning approaches, both reaching 96%, while the DistilBERT and SciBERT models also attained 96% accuracy in the deep learning category. These findings underscore the importance of hyperparameter tuning in maximizing model performance. This study contributes to the ongoing research on AI-assisted diagnostic tools in mental health by providing insights into the efficacy of different model architectures and data balancing methods.
2024
Fernández-Morales, Enrique; Sanchez-Bocanegra, Carlos L.; Pastor-Vargas, Rafael; Pereyra-Rodriguez, José-Juan; Haut, Juan M.; Benítez-Andrades, José Alberto
Analysis and Detection of Melanoma through Collective Intelligence with AI Proceedings Article
En: 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), pp. 75–80, 2024, (ISSN: 2372-9198).
@inproceedings{fernandez-morales_analysis_2024,
title = {Analysis and Detection of Melanoma through Collective Intelligence with AI},
author = {Enrique Fernández-Morales and Carlos L. Sanchez-Bocanegra and Rafael Pastor-Vargas and José-Juan Pereyra-Rodriguez and Juan M. Haut and José Alberto Benítez-Andrades},
url = {https://ieeexplore.ieee.org/document/10600810},
doi = {10.1109/CBMS61543.2024.00021},
year = {2024},
date = {2024-06-01},
urldate = {2025-08-30},
booktitle = {2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {75–80},
abstract = {Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.},
note = {ISSN: 2372-9198},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
Cancer is a widespread global health problem, claiming millions of lives each year, and skin cancer represents a significant threat as it is one of the most common types. Early tumor detection via medical imaging is critical for effective treatment. Leveraging artificial intelligence, particularly novel models like Transformers, presents promising avenues for improved diagnosis. This paper explores the efficacy of a Collective Intelligence approach using AI in classifying cancerous and non-cancerous tumors, aiming to reduce classification errors and support clinical decision-making. We created five different configurations using various datasets to compare the results. The results show solid performance for the CI in the evaluated tasks, reaching up to 75.89% accuracy. The lack of images in certain classes significantly contributes to overfitting. It is suggested to explore data expansion strategies and improve consistency in image capture for future work.
Benítez-Andrades, José Alberto; Ordás-Reyes, Nicolás; García, Antonio Serrano; Blanco, Marta Esteban; Nicolás, Jesús Betegón; Gutiérrez, José Viloria; Encinas, José Ángel Hernández; Muñoz, Ana Lozano; Corcoba, Alicia Merayo; Prada-García, Camino
Machine Learning in Predicting the Success of Spine Surgery: A Multivariable Study Proceedings Article
En: 2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS), pp. 303–308, 2024, (ISSN: 2372-9198).
@inproceedings{benitez-andrades_machine_2024,
title = {Machine Learning in Predicting the Success of Spine Surgery: A Multivariable Study},
author = {José Alberto Benítez-Andrades and Nicolás Ordás-Reyes and Antonio Serrano García and Marta Esteban Blanco and Jesús Betegón Nicolás and José Viloria Gutiérrez and José Ángel Hernández Encinas and Ana Lozano Muñoz and Alicia Merayo Corcoba and Camino Prada-García},
url = {https://ieeexplore.ieee.org/document/10600965},
doi = {10.1109/CBMS61543.2024.00057},
year = {2024},
date = {2024-06-01},
urldate = {2025-08-30},
booktitle = {2024 IEEE 37th International Symposium on Computer-Based Medical Systems (CBMS)},
pages = {303–308},
abstract = {This study explores the application of Artificial Intelligence (AI) in spine surgery, with a focus on enhancing precision and accuracy in outcome prediction. Leveraging machine learning (ML) models – including GaussianNB, ComplementNB, KNN, and Decision Trees – we analyze a rich dataset derived from 244 spine surgery patients. This dataset comprises 24 diverse variables, capturing elements such as pre-surgical conditions, socioeconomic status, psychometric evaluations, and various analytical metrics. Notably, one critical variable is the surgery’s success, serving as the primary outcome for prediction.The data was meticulously categorized into seven distinct groups, reflecting various aspects of the surgical process and patient backgrounds. This structured approach enabled a targeted and nuanced analysis, deepening our understanding of the key factors instrumental in predicting surgical outcomes. We employed a stratified split methodology for our dataset, dedicating 80% to training and 20% to testing. This was supplemented by 5-fold cross-validation and an extensive grid search optimization for refining the KNN and Decision Trees models.Our results underscore the profound capability of AI in predicting the outcomes of spine surgeries. The KNN model showed remarkable proficiency, particularly in analyzing groups defined by pre-surgical and analytical variables, demonstrating its prowess in handling complex medical datasets. This study not only evidences the effectiveness of specific ML models in medical prognostics but also highlights AI’s transformative potential in healthcare. It underlines the critical role of AI in advancing medical diagnostics and decision-making for surgeries that entail multifaceted data analysis. These insights pave the way for future research into the broader application of AI in medicine, promising more personalized and effective treatment strategies and effective treatment approaches.},
note = {ISSN: 2372-9198},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
This study explores the application of Artificial Intelligence (AI) in spine surgery, with a focus on enhancing precision and accuracy in outcome prediction. Leveraging machine learning (ML) models – including GaussianNB, ComplementNB, KNN, and Decision Trees – we analyze a rich dataset derived from 244 spine surgery patients. This dataset comprises 24 diverse variables, capturing elements such as pre-surgical conditions, socioeconomic status, psychometric evaluations, and various analytical metrics. Notably, one critical variable is the surgery’s success, serving as the primary outcome for prediction.The data was meticulously categorized into seven distinct groups, reflecting various aspects of the surgical process and patient backgrounds. This structured approach enabled a targeted and nuanced analysis, deepening our understanding of the key factors instrumental in predicting surgical outcomes. We employed a stratified split methodology for our dataset, dedicating 80% to training and 20% to testing. This was supplemented by 5-fold cross-validation and an extensive grid search optimization for refining the KNN and Decision Trees models.Our results underscore the profound capability of AI in predicting the outcomes of spine surgeries. The KNN model showed remarkable proficiency, particularly in analyzing groups defined by pre-surgical and analytical variables, demonstrating its prowess in handling complex medical datasets. This study not only evidences the effectiveness of specific ML models in medical prognostics but also highlights AI’s transformative potential in healthcare. It underlines the critical role of AI in advancing medical diagnostics and decision-making for surgeries that entail multifaceted data analysis. These insights pave the way for future research into the broader application of AI in medicine, promising more personalized and effective treatment strategies and effective treatment approaches.
González-Castro, Ana; Leirós-Rodríguez, Raquel; Prada-García, Camino; Benítez-Andrades, José Alberto
The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review Artículo de revista
En: Journal of Medical Internet Research, vol. 26, no 1, pp. e54934, 2024, (Company: Journal of Medical Internet Research Distributor: Journal of Medical Internet Research Institution: Journal of Medical Internet Research Label: Journal of Medical Internet Research Publisher: JMIR Publications Inc., Toronto, Canada).
@article{gonzalez-castro_applications_2024,
title = {The Applications of Artificial Intelligence for Assessing Fall Risk: Systematic Review},
author = {Ana González-Castro and Raquel Leirós-Rodríguez and Camino Prada-García and José Alberto Benítez-Andrades},
url = {https://www.jmir.org/2024/1/e54934},
doi = {10.2196/54934},
year = {2024},
date = {2024-04-01},
urldate = {2025-08-30},
journal = {Journal of Medical Internet Research},
volume = {26},
number = {1},
pages = {e54934},
abstract = {Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.
Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.
Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.
Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.
Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.
Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv},
note = {Company: Journal of Medical Internet Research
Distributor: Journal of Medical Internet Research
Institution: Journal of Medical Internet Research
Label: Journal of Medical Internet Research
Publisher: JMIR Publications Inc., Toronto, Canada},
keywords = {},
pubstate = {published},
tppubtype = {article}
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Background: Falls and their consequences are a serious public health problem worldwide. Each year, 37.3 million falls requiring medical attention occur. Therefore, the analysis of fall risk is of great importance for prevention. Artificial intelligence (AI) represents an innovative tool for creating predictive statistical models of fall risk through data analysis.
Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.
Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.
Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.
Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.
Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv
Objective: The aim of this review was to analyze the available evidence on the applications of AI in the analysis of data related to postural control and fall risk.
Methods: A literature search was conducted in 6 databases with the following inclusion criteria: the articles had to be published within the last 5 years (from 2018 to 2024), they had to apply some method of AI, AI analyses had to be applied to data from samples consisting of humans, and the analyzed sample had to consist of individuals with independent walking with or without the assistance of external orthopedic devices.
Results: We obtained a total of 3858 articles, of which 22 were finally selected. Data extraction for subsequent analysis varied in the different studies: 82% (18/22) of them extracted data through tests or functional assessments, and the remaining 18% (4/22) of them extracted through existing medical records. Different AI techniques were used throughout the articles. All the research included in the review obtained accuracy values of >70% in the predictive models obtained through AI.
Conclusions: The use of AI proves to be a valuable tool for creating predictive models of fall risk. The use of this tool could have a significant socioeconomic impact as it enables the development of low-cost predictive models with a high level of accuracy.
Trial Registration: PROSPERO CRD42023443277; https://tinyurl.com/4sb72ssv
Puente-Hidalgo, Sara; Prada-García, Camino; Benítez-Andrades, José Alberto; Fernández-Martínez, Elena
Promotion of Healthy Habits in University Students: Literature Review Artículo de revista
En: Healthcare, vol. 12, no 10, pp. 993, 2024, ISSN: 2227-9032, (Publisher: Multidisciplinary Digital Publishing Institute).
@article{puente-hidalgo_promotion_2024,
title = {Promotion of Healthy Habits in University Students: Literature Review},
author = {Sara Puente-Hidalgo and Camino Prada-García and José Alberto Benítez-Andrades and Elena Fernández-Martínez},
url = {https://www.mdpi.com/2227-9032/12/10/993},
doi = {10.3390/healthcare12100993},
issn = {2227-9032},
year = {2024},
date = {2024-01-01},
urldate = {2025-08-30},
journal = {Healthcare},
volume = {12},
number = {10},
pages = {993},
abstract = {The increase in responsibilities, together with the multiple challenges that students face in the university period, has a direct impact on their healthy lifestyles. This literature review describes the benefits of promoting healthy habits in college, highlighting the fundamental role of prevention and promotion. A systematic review was carried out following the PRISMA recommendations, searching for information in the WOS and Scopus databases. On the other hand, a search was carried out within the existing and available grey literature. The review focused on finding information about physical activity, nutrition, and stress (with an emphasis on resilience and academic burnout) in university students. This bibliographic review includes 32 articles and six web pages, containing information on the benefits of physical activity, healthy habits, and health prevention. The information collected in this study shows that university students are exposed to multiple changes during this period, increasing as the academic years progress. At that time, their habits worsen, with low adherence to the Mediterranean diet, low physical activity, and high levels of stress, specifically increasing cases of academic burnout. The establishment of healthy habits during the university period is necessary, observing an improvement in all the variables studied. Prevention has played a fundamental role.},
note = {Publisher: Multidisciplinary Digital Publishing Institute},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The increase in responsibilities, together with the multiple challenges that students face in the university period, has a direct impact on their healthy lifestyles. This literature review describes the benefits of promoting healthy habits in college, highlighting the fundamental role of prevention and promotion. A systematic review was carried out following the PRISMA recommendations, searching for information in the WOS and Scopus databases. On the other hand, a search was carried out within the existing and available grey literature. The review focused on finding information about physical activity, nutrition, and stress (with an emphasis on resilience and academic burnout) in university students. This bibliographic review includes 32 articles and six web pages, containing information on the benefits of physical activity, healthy habits, and health prevention. The information collected in this study shows that university students are exposed to multiple changes during this period, increasing as the academic years progress. At that time, their habits worsen, with low adherence to the Mediterranean diet, low physical activity, and high levels of stress, specifically increasing cases of academic burnout. The establishment of healthy habits during the university period is necessary, observing an improvement in all the variables studied. Prevention has played a fundamental role.
Rubio-Martín, Sergio; García-Ordás, María Teresa; Bayón-Gutiérrez, Martín; Prieto-Fernández, Natalia; Benítez-Andrades, José Alberto
Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing Artículo de revista
En: Health Information Science and Systems, vol. 12, no 1, pp. 20, 2024, ISSN: 2047-2501.
@article{rubio-martin_enhancing_2024,
title = {Enhancing ASD detection accuracy: a combined approach of machine learning and deep learning models with natural language processing},
author = {Sergio Rubio-Martín and María Teresa García-Ordás and Martín Bayón-Gutiérrez and Natalia Prieto-Fernández and José Alberto Benítez-Andrades},
url = {https://doi.org/10.1007/s13755-024-00281-y},
doi = {10.1007/s13755-024-00281-y},
issn = {2047-2501},
year = {2024},
date = {2024-03-01},
urldate = {2025-08-30},
journal = {Health Information Science and Systems},
volume = {12},
number = {1},
pages = {20},
abstract = {The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
The main aim of our study was to explore the utility of artificial intelligence (AI) in diagnosing autism spectrum disorder (ASD). The study primarily focused on using machine learning (ML) and deep learning (DL) models to detect ASD potential cases by analyzing text inputs, especially from social media platforms like Twitter. This is to overcome the ongoing challenges in ASD diagnosis, such as the requirement for specialized professionals and extensive resources. Timely identification, particularly in children, is essential to provide immediate intervention and support, thereby improving the quality of life for affected individuals.
García-Ordás, María Teresa; Benítez-Andrades, José Alberto; Aveleira-Mata, Jose; Alija-Pérez, José-Manuel; Benavides, Carmen
Determining the severity of Parkinson’s disease in patients using a multi task neural network Artículo de revista
En: Multimedia Tools and Applications, vol. 83, no 2, pp. 6077–6092, 2024, ISSN: 1573-7721.
@article{garcia-ordas_determining_2024,
title = {Determining the severity of Parkinson’s disease in patients using a multi task neural network},
author = {María Teresa García-Ordás and José Alberto Benítez-Andrades and Jose Aveleira-Mata and José-Manuel Alija-Pérez and Carmen Benavides},
url = {https://doi.org/10.1007/s11042-023-14932-x},
doi = {10.1007/s11042-023-14932-x},
issn = {1573-7721},
year = {2024},
date = {2024-01-01},
urldate = {2025-08-30},
journal = {Multimedia Tools and Applications},
volume = {83},
number = {2},
pages = {6077–6092},
abstract = {Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Parkinson’s disease is easy to diagnose when it is advanced, but it is very difficult to diagnose in its early stages. Early diagnosis is essential to be able to treat the symptoms. It impacts on daily activities and reduces the quality of life of both the patients and their families and it is also the second most prevalent neurodegenerative disorder after Alzheimer in people over the age of 60. Most current studies on the prediction of Parkinson’s severity are carried out in advanced stages of the disease. In this work, the study analyzes a set of variables that can be easily extracted from voice analysis, making it a very non-intrusive technique. In this paper, a method based on different deep learning techniques is proposed with two purposes. On the one hand, to find out if a person has severe or non-severe Parkinson’s disease, and on the other hand, to determine by means of regression techniques the degree of evolution of the disease in a given patient. The UPDRS (Unified Parkinson’s Disease Rating Scale) has been used by taking into account both the motor and total labels, and the best results have been obtained using a mixed multi-layer perceptron (MLP) that classifies and regresses at the same time and the most important features of the data obtained are taken as input, using an autoencoder. A success rate of 99.15% has been achieved in the problem of predicting whether a person suffers from severe Parkinson’s disease or non-severe Parkinson’s disease. In the degree of disease involvement prediction problem case, a MSE (Mean Squared Error) of 0.15 has been obtained. Using a full deep learning pipeline for data preprocessing and classification has proven to be very promising in the field Parkinson’s outperforming the state-of-the-art proposals.
Benítez-Andrades, José Alberto; Prada-García, Camino; García-Fernández, Rubén; Ballesteros-Pomar, María D; González-Alonso, María-Inmaculada; Serrano-García, Antonio
Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study Artículo de revista
En: DIGITAL HEALTH, vol. 10, pp. 20552076241239274, 2024, ISSN: 2055-2076, (Publisher: SAGE Publications Ltd).
@article{benitez-andrades_application_2024,
title = {Application of machine learning algorithms in classifying postoperative success in metabolic bariatric surgery: Acomprehensive study},
author = {José Alberto Benítez-Andrades and Camino Prada-García and Rubén García-Fernández and María D Ballesteros-Pomar and María-Inmaculada González-Alonso and Antonio Serrano-García},
url = {https://doi.org/10.1177/20552076241239274},
doi = {10.1177/20552076241239274},
issn = {2055-2076},
year = {2024},
date = {2024-09-01},
urldate = {2025-08-30},
journal = {DIGITAL HEALTH},
volume = {10},
pages = {20552076241239274},
abstract = {ObjectivesMetabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types.MethodsVarious machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques.ResultsExperimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results.ConclusionsThe study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.},
note = {Publisher: SAGE Publications Ltd},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
ObjectivesMetabolic bariatric surgery is a critical intervention for patients living with obesity and related health issues. Accurate classification and prediction of patient outcomes are vital for optimizing treatment strategies. This study presents a novel machine learning approach to classify patients in the context of metabolic bariatric surgery, providing insights into the efficacy of different models and variable types.MethodsVarious machine learning models, including Gaussian Naive Bayes, Complement Naive Bayes, K-nearest neighbour, Decision Tree, K-nearest neighbour with RandomOverSampler, and K-nearest neighbour with SMOTE, were applied to a dataset of 73 patients. The dataset, comprising psychometric, socioeconomic, and analytical variables, was analyzed to determine the most efficient predictive model. The study also explored the impact of different variable groupings and oversampling techniques.ResultsExperimental results indicate average accuracy values as high as 66.7% for the best model. Enhanced versions of K-nearest neighbour and Decision Tree, along with variations of K-nearest neighbour such as RandomOverSampler and SMOTE, yielded the best results.ConclusionsThe study unveils a promising avenue for classifying patients in the realm of metabolic bariatric surgery. The results underscore the importance of selecting appropriate variables and employing diverse approaches to achieve optimal performance. The developed system holds potential as a tool to assist healthcare professionals in decision-making, thereby enhancing metabolic bariatric surgery outcomes. These findings lay the groundwork for future collaboration between hospitals and healthcare entities to improve patient care through the utilization of machine learning algorithms. Moreover, the findings suggest room for improvement, potentially achievable with a larger dataset and careful parameter tuning.
