This line of work is framed in the area of Bioinformatics and aims to provide novel solutions to problems related to precision medicine and offer help in the investigation of the mechanisms inherent to diseases. In this way, the application of AI techniques, more specifically Machine Learning (ML), and existing computing capabilities to the field of health will make it possible to generate support systems for physicians that facilitate their work and the quality of the services provided, resulting in a better quality of life for patients. Thus, the aim is to apply techniques that have proven their validity and effectiveness in other fields, such as biometrics, handwritten text recognition, and automatic translation to healthcare.
Pedro Pons-Suñer, Laura Arnal Benedicto, François Signol, M. Jose Caballero Mateos, Bernardo Valdivieso Martinez, Juan-Carlos Perez-Cortes. (2023).
Prediction of 30-day unplanned hospital readmission through survival analysis.
HELIYON, .
https://doi.org/10.1016/j.heliyon.2023.e20942
Prediction of 30-day unplanned hospital readmission through survival analysis.
HELIYON, .
https://doi.org/10.1016/j.heliyon.2023.e20942
José Ramón Navarro Cerdán, Manuel Sánchez-Gomis, Patricia Pons, Santiago Gálvez-Settier, et al.. (2023).
Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks.
Health Informatics Journal 29(4), .
https://doi.org/10.1177/14604582231212494
Towards a personalized health care using a divisive hierarchical clustering approach for comorbidity and the prediction of conditioned group risks.
Health Informatics Journal 29(4), .
https://doi.org/10.1177/14604582231212494
François Signol, Laura Arnal, J. Ramón Navarro-Cerdán, Rafael Llobet, Joaquim Arlandis, Juan-Carlos Perez-Cortes. (2023).
SEQENS: an ensemble method for relevant gene identification in microarray data.
Computers-in-biology-and-medicine, .
https://doi.org/10.1016/j.compbiomed.2022.106413
SEQENS: an ensemble method for relevant gene identification in microarray data.
Computers-in-biology-and-medicine, .
https://doi.org/10.1016/j.compbiomed.2022.106413
Laura Arnal, Pedro Pons-Suñer, J.Ramón Navarro-Cerdán, Pablo Ruiz-Valls, Mª José Caballero Mateos, Bernardo Valdivieso Martínez, Juan-Carlos Perez-Cortes. (2022).
Decision support through risk cost estimation in 30-day hospital unplanned readmission.
PLOS ONE 17(7), .
https://doi.org/10.1371/journal.pone.0271331
Decision support through risk cost estimation in 30-day hospital unplanned readmission.
PLOS ONE 17(7), .
https://doi.org/10.1371/journal.pone.0271331
Colet, O, Del Tejo Catalá, O, Navarro-Cerdán, J, Morán, E, Bolón Marset, J, Pérez-Cortés, Llobet, R, Martinez-Cuenca, E, Bonillo, M, Arlandis, S. (2022).
133 CAN WE SPARE PRESSURE-FLOW STUDIES? UROFLOWMETRY PATTERN RECOGNITION USING COMPUTATIONAL PROCESSING: PRELIMINARY RESULTS.
ICS 2022 Vienna Abstracts, .
https://doi.org/10.1016/j.cont.2022.100245
133 CAN WE SPARE PRESSURE-FLOW STUDIES? UROFLOWMETRY PATTERN RECOGNITION USING COMPUTATIONAL PROCESSING: PRELIMINARY RESULTS.
ICS 2022 Vienna Abstracts, .
https://doi.org/10.1016/j.cont.2022.100245
H. Lopez-Almazan, F.J. Pérez-Benito, A. Larroza, J-C. Perez-Cortés, M. Pollán, B. Pérez-Gómez, D. Salas-Trejo, M. Casals, R. Llobet. (2022).
A Deep Learning Framework to classify Breast Density with Noisy Labels Regularization.
Computer Methods and Programs in Biomedicine 221, 106885.
https://doi.org/10.1016/j.cmpb.2022.106885
A Deep Learning Framework to classify Breast Density with Noisy Labels Regularization.
Computer Methods and Programs in Biomedicine 221, 106885.
https://doi.org/10.1016/j.cmpb.2022.106885
A. Larroza, F.J. Pérez-Benito, J-C. Perez-Cortes, M. Román, M. Pollán, B. Pérez-Gómez, D. Salas-Trejo, M. Casals, R. Llobet. (2022).
Breast Dense Tissue Segmentation With Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach.
Diagnostics, .
https://doi.org/10.3390/diagnostics12081822
Breast Dense Tissue Segmentation With Noisy Labels: A Hybrid Threshold-Based and Mask-Based Approach.
Diagnostics, .
https://doi.org/10.3390/diagnostics12081822
Andrés Larroza. (2022).
Automatic estimation of breast density.
Blog ITI, .
https://www.iti.es/blog/estimacion-automatica-de-la-densidad-mamaria/
Automatic estimation of breast density.
Blog ITI, .
https://www.iti.es/blog/estimacion-automatica-de-la-densidad-mamaria/
S. Aceituno, F. Aparicio, C. Expósito, L. Martínez, S. Giménez-Campos, J. Doménech-Pascual, V Ruiz-Garcia, J. Garcés-Ferrer, F. Ródenas-Rigla, Laura Arnal, Juan-Carlos Perez-Cortes, F. Pérez-Sádaba. (2022).
Co-designing a mHealth app for the collection of patient-reported outcomes in frail patients.
22nd International Conference on Integrated Care (ICIC22), .
http://dx.doi.org/10.5334/ijic.ICIC22347
Co-designing a mHealth app for the collection of patient-reported outcomes in frail patients.
22nd International Conference on Integrated Care (ICIC22), .
http://dx.doi.org/10.5334/ijic.ICIC22347
Francisco Ródenas-Rigla, María-Eugenia Gas-López, Jorge Garcés-Ferrer, Carlos López-Gómez, Juan-Ramón Doménech-Pascual, Laura Arnal, Juan-Carlos Perez-Cortes, Luis Lizán, Francisco Pérez-Sádaba, Carlos Expósito, Lorenzo Martínez, Fernando Aparicio. (2022).
Use of artificial intelligence to improve the care of frail older patients with chronic diseases: Application to identify the 30 risk of hospital readmission and predict hospital clinical pathways.
22nd International Conference on Integrated Care (ICIC22), .
http://dx.doi.org/10.5334/ijic.ICIC22088
Use of artificial intelligence to improve the care of frail older patients with chronic diseases: Application to identify the 30 risk of hospital readmission and predict hospital clinical pathways.
22nd International Conference on Integrated Care (ICIC22), .
http://dx.doi.org/10.5334/ijic.ICIC22088