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.
Juan A. Delgado Sanchis, François Signol, Juan-Carlos Perez-Cortes, Eva M. García-López, Salvador Mena-Mollá, Nieves Carbonell-Monleón, María Rodríguez-Gimillo, José Luis García-Giménez. (2022).
A machine learning based predictive model for the diagnosis of sepsis.
medRxiv, Cold Spring Harbor Laboratory, preprint.
https://doi.org/10.1101/2022.04.29.22274361
A machine learning based predictive model for the diagnosis of sepsis.
medRxiv, Cold Spring Harbor Laboratory, preprint.
https://doi.org/10.1101/2022.04.29.22274361
Omar del Tejo Catalá, Ismael Salvador Igual, F.J. Pérez-Benito, David Millán Escrivá, Vicent Ortiz, Rafael Llobet Azpitarte, Juan Carlos Pérez Cortés. (2021).
Bias analysis on public x-ray images datasets of pneumonia and covid-19 patients.
IEEE Access 9, 42370-42383.
https://doi.org/10.1109/ACCESS.2021.3065456
Bias analysis on public x-ray images datasets of pneumonia and covid-19 patients.
IEEE Access 9, 42370-42383.
https://doi.org/10.1109/ACCESS.2021.3065456
Rafael Ortiz-Ramón, Silvia Ruiz-España, Enrique Mollá-Olmos, David Moratal. (2020).
Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach.
Physica Medica 76, 44-54.
Glioblastomas and brain metastases differentiation following an MRI texture analysis-based radiomics approach.
Physica Medica 76, 44-54.
F.J. Pérez-Benito, F. Signol, J-C Perez-Cortes, A Fuster-Baggetto, M. Pollan, B. Pérez-Gómez, D. Salas-Trejo, M. Casals, I. Martínez, R. Llobet. (2020).
A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.
Computer Methods and Programs in Biomedicine 195(105668), .
https://doi.org/10.1016/j.cmpb.2020.105668
A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation.
Computer Methods and Programs in Biomedicine 195(105668), .
https://doi.org/10.1016/j.cmpb.2020.105668
F.J. Pérez-Benito, J.A. Conejero, C. Sáez, J.M. García-Gómez, E. Navarro-Pardo, L.L. Florencio, C. Fernández-de-las-Peñas. (2020).
Subgrouping Factors Incluencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry.
PAIN Practice 20(3), 297-309.
https://doi.org/10.1111/papr.12854
Subgrouping Factors Incluencing Migraine Intensity in Women: A Semi-automatic Methodology Based on Machine Learning and Information Geometry.
PAIN Practice 20(3), 297-309.
https://doi.org/10.1111/papr.12854
F.J. Pérez-Benito, P. Villacampa-Fernández, J. A. Conejero, J.M. García-Gómez, E. Navarro-Pardo. (2019).
A happiness degree predictor using the conceptual data structure for deep learning architectures.
Computer Methods and Programs in Biomedicine 168, 59-68.
https://doi.org/10.1016/j.cmpb.2017.11.004
A happiness degree predictor using the conceptual data structure for deep learning architectures.
Computer Methods and Programs in Biomedicine 168, 59-68.
https://doi.org/10.1016/j.cmpb.2017.11.004
F.J. Pérez-Benito, C. Sáez, J.A. Conejero, S. Tortajada, B. Valdivieso, J.M. García-Gómez. (2019).
Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years.
PloS One 14(8), e0220369.
https://doi.org/10.1371/journal.pone.0220369
Temporal variability analysis reveals biases in electronic health records due to hospital process reengineering interventions over seven years.
PloS One 14(8), e0220369.
https://doi.org/10.1371/journal.pone.0220369
F.J. Pérez-Benito, F. Signol, J-C. Perez-Cortes, M. Pollan, B. Pérez-Gómez, D. Salas-Trejo, M. Casals, I. Martínez, R. Llobet. (2019).
Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
Computer Methods and Programs in Biomedicine 177, 123-132.
https://doi.org/10.1016/j.cmpb.2019.05.022
Global parenchymal texture features based on histograms of oriented gradients improve cancer development risk estimation from healthy breasts.
Computer Methods and Programs in Biomedicine 177, 123-132.
https://doi.org/10.1016/j.cmpb.2019.05.022
Rafael Ortiz-Ramón, Andrés Larroza, Silvia Ruiz-España, Estanislao Arana, David Moratal. (2018).
Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.
European Radiology 28, 4514–4523.
Classifying brain metastases by their primary site of origin using a radiomics approach based on texture analysis: a feasibility study.
European Radiology 28, 4514–4523.
M. d P. del Pozo, A. Castelló, C. Vidal, D. Salas-Trejo, C. Sánchez-Contador, C. Pedraz-Pingarrón, C. Santamariña, M. Ederra, R. Llobet, J. Vioque, B. Pérez-Gómez, M. Pollán, V. Lope. (2018).
Overeating, caloric restriction and mammographic density in spanish women.
Maturitas 117, 57-63.
https://doi.org/10.1016/j.maturitas.2018.09.006
Overeating, caloric restriction and mammographic density in spanish women.
Maturitas 117, 57-63.
https://doi.org/10.1016/j.maturitas.2018.09.006