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David Chushig-Muzo
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Journal Articles
- 2024
- [j4]David Chushig-Muzo
, Hugo Calero-Díaz
Interpretable Data-Driven Approach Based on Feature Selection Methods and GAN-Based Models for Cardiovascular Risk Prediction in Diabetic Patients. IEEE Access 12: 84292-84305 (2024) - 2022
- [j3]David Chushig-Muzo
Learning and visualizing chronic latent representations using electronic health records. BioData Min. 15(1) (2022) - 2021
- [j2]David Chushig-Muzo
Interpreting clinical latent representations using autoencoders and probabilistic models. Artif. Intell. Medicine 122: 102211 (2021) - 2020
- [j1]David Chushig-Muzo
Data-Driven Visual Characterization of Patient Health-Status Using Electronic Health Records and Self-Organizing Maps. IEEE Access 8: 137019-137031 (2020)
Conference and Workshop Papers
- 2023
- [c4]María Teresa Jurado-Camino
On the Use of Generative Adversarial Networks to Predict Health Status Among Chronic Patients. HEALTHINF 2023: 167-178 - 2022
- [c3]Hugo Calero-Díaz
Data-driven cardiovascular risk prediction and prognosis factor identification in diabetic patients. BHI 2022: 1-4 - [c2]Clara García-Vicente
Clinical Synthetic Data Generation to Predict and Identify Risk Factors for Cardiovascular Diseases. Poly/DMAH@VLDB 2022: 75-91 - [c1]Hugo Calero-Díaz
Characterizing Cardiovascular Risk Through Unsupervised and Interpretable Techniques. IDEAL 2022: 22-30
Informal and Other Publications
- 2024
- [i1]Vanesa Gómez-Martínez, Francisco J. Lara-Abelenda, Pablo Peiro-Corbacho, David Chushig-Muzo, Conceição Granja, Cristina Soguero-Ruíz:
LM-IGTD: a 2D image generator for low-dimensional and mixed-type tabular data to leverage the potential of convolutional neural networks. CoRR abs/2406.14566 (2024)
Coauthor Index
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last updated on 2024-10-07 22:19 CEST by the dblp team
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