Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps. - David Chushig-Muzo; Cristina Soguero-Ruíz; Andries P. Engelbrecht; Pablo de Miguel-Bohoyo; Inmaculada Mora-Jiménez (IEEE Access, 2020)

Interpreting clinical latent representations using autoencoders and probabilistic models. - David Chushig-Muzo; Cristina Soguero-Ruíz; Pablo de Miguel-Bohoyo; Inmaculada Mora-Jiménez (Artif. Intell. Medicine, 2021)

Data-driven cardiovascular risk prediction and prognosis factor identification in diabetic patients. - Hugo Calero-Díaz; David Chushig-Muzo; Himar Fabelo; Inmaculada Mora-Jiménez; Conceição Granja; Cristina Soguero-Ruíz (BHI, 2022)

Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases. - Clara García-Vicente; David Chushig-Muzo; Inmaculada Mora-Jiménez; Himar Fabelo; Inger Torhild Gram; Maja-Lisa Løchen; Conceição Granja; Cristina Soguero-Ruíz (Poly/DMAH@VLDB, 2022)

Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques. - Hugo Calero-Díaz; David Chushig-Muzo; Cristina Soguero-Ruíz (IDEAL, 2022)

Learning and visualizing chronic latent representations using electronic health records. - David Chushig-Muzo; Cristina Soguero-Ruíz; Pablo de Miguel-Bohoyo; Inmaculada Mora-Jiménez (BioData Min., 2022)

On the Use of Generative Adversarial Networks to Predict Health Status Among Chronic Patients. - María Teresa Jurado-Camino; David Chushig-Muzo; Cristina Soguero-Ruíz; Pablo de Miguel-Bohoyo; Inmaculada Mora-Jiménez (HEALTHINF, 2023)

Interpretable Data-Driven Approach Based on Feature Selection Methods and GAN-Based Models for Cardiovascular Risk Prediction in Diabetic Patients. - David Chushig-Muzo; Hugo Calero-Díaz; Francisco J. Lara-Abelenda; Vanesa Gómez-Martínez; Conceição Granja; Cristina Soguero-Ruíz (IEEE Access, 2024)

Ensemble feature selection and tabular data augmentation with generative adversarial networks to enhance cutaneous melanoma identification and interpretability. - Vanesa Gómez-Martínez; David Chushig-Muzo; Marit B. Veierød; Conceição Granja; Cristina Soguero-Ruíz (BioData Min., 2024)

LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks. - Vanesa Gómez-Martínez; Francisco J. Lara-Abelenda; Pablo Peiro-Corbacho; David Chushig-Muzo; Conceição Granja; Cristina Soguero-Ruíz (CoRR, 2024)

Evaluating Time Series Classification Models for Nocturnal Hypoglycemia: From Predictive Performance to Environmental Impact. - Francisco J. Lara-Abelenda; David Chushig-Muzo; Carmelo Betancort; Ana M. Wägner; Conceição Granja; Cristina Soguero-Ruíz (IEEE Access, 2025)

Personalized glucose forecasting for people with type 1 diabetes using large language models. - Francisco J. Lara-Abelenda; David Chushig-Muzo; Pablo Peiro-Corbacho; Ana M. Wägner; Conceição Granja; Cristina Soguero-Ruíz (Comput. Methods Programs Biomed., 2025)

Interpretable and multimodal fusion methodology to predict severe hypoglycemia in adults with type 1 diabetes. - Francisco J. Lara-Abelenda; David Chushig-Muzo; Ana M. Wägner; Maryam Tayefi; Cristina Soguero-Ruíz (Eng. Appl. Artif. Intell., 2025)

Transfer learning for a tabular-to-image approach: A case study for cardiovascular disease prediction. - Francisco J. Lara-Abelenda; David Chushig-Muzo; Pablo Peiro-Corbacho; Vanesa Gómez-Martínez; Ana M. Wägner; Conceição Granja; Cristina Soguero-Ruíz (J. Biomed. Informatics, 2025)