AVANCES EN TECNICAS DE INTELIGENCIA COMPUTACIONAL PARA EL PROCESO DE SENSORES MULTIPLES PORTABLES PARA APLICACIONES BIOMEDICAS, EN NEUROCIENCIAS Y DE INTERACCION ROBOTICA

PID2020-116346GB-I00

Nombre agencia financiadora Agencia Estatal de Investigación
Acrónimo agencia financiadora AEI
Programa Programa Estatal de Generación de Conocimiento y Fortalecimiento Científico y Tecnológico del Sistema de I+D+i
Subprograma Subprograma Estatal de Generación de Conocimiento
Convocatoria Proyectos I+D
Año convocatoria 2020
Unidad de gestión Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020
Centro beneficiario UNIVERSIDAD DEL PAIS VASCO EUSKAL HERRIKO UNIBERTSITATEA
Identificador persistente http://dx.doi.org/10.13039/501100011033

Publicaciones

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Mortality risks after two years in frail and pre-frail older adults admitted to hospital

Academica-e. Repositorio Institucional de la Universidad Pública de Navarra
  • Cano-Escalera, Guillermo
  • Graña, Manuel
  • Irazusta, Jon
  • Labayen Goñi, Idoia
  • González Pinto, Ana
  • Besga, Ariadna
Background: Frailty is characterized by a progressive decline in the physiological functions
of multiple body systems that lead to a more vulnerable condition, which is prone to the development
of various adverse events, such as falls, hospitalization, and mortality. This study aims to determine
whether frailty increases mortality compared to pre-frailty and to identify variables associated with a
higher risk of mortality. Materials: Two cohorts, frail and pre-frail subjects, are evaluated according
to the Fried phenotype. A complete examination of frailty, cognitive status, comorbidities and
pharmacology was carried out at hospital admission and was extracted through electronic health
record (EHR). Mortality was evaluated from the EHR. Methods: Kaplan–Meier estimates of survival
probability functions were calculated at two years censoring time for frail and pre-frail cohorts. The
log-rank test assessed significant differences between survival probability functions. Significant
variables for frailty (p < 0–05) were extracted by independent sample t-test. Further selection was
based on variable significance found in multivariate logistic regression discrimination between
frail and pre-frail subjects. Cox regression over univariate t-test-selected variables was calculated
to identify variables associated with higher proportional hazard risks (HR) at two years. Results:
Frailty is associated with greater mortality at two years censoring time than pre-frailty (log-rank test,
p < 0.0001). Variables with significant (p < 0.05) association with mortality identified in both cohorts
(HR 95% (CI in the frail cohort) are male sex (0.44 (0.29–0.66)), age (1.05 (1.01–1.09)), weight (0.98
(0.96–1.00)), and use of proton-pump inhibitors (PPIs) (0.60 (0.41–0.87)). Specific high-risk factors
in the frail cohort are readmission at 30 days (0.50 (0.33–0.74)), SPPB sit and stand (0.62 (0.45–0.85)),
heart failure (0.67 (0.46–0.98)), use of antiplatelets (1.80 (1.19–2.71)), and quetiapine (0.31 (0.12–0.81)).
Specific high-risk factors in the pre-frail cohort are Barthel’s score (120 (7.7–1700)), Pfeiffer test (8.4;
(2.3–31)), Mini Nutritional Assessment (MNA) (1200 (18–88,000)), constipation (0.025 (0.0027–0.24)),
falls (18,000 (150–2,200,000)), deep venous thrombosis (8400 (19–3,700,000)), cerebrovascular disease
(0.01 (0.00064–0.16)), diabetes (360 (3.4–39,000)), thyroid disease (0.00099 (0.000012–0.085)), and the
use of PPIs (0.062 (0.0072–0.54)), Zolpidem (0.000014 (0.0000000021–0.092)), antidiabetics (0.00015
(0.00000042–0.051)), diuretics (0.0003 (0.000004–0.022)), and opiates (0.000069 (0.00000035–0.013)).
Conclusions: Frailty is associated with higher mortality at two years than pre-frailty. Frailty is
recognized as a systemic syndrome with many links to older-age comorbidities, which are also found
in our study. Polypharmacy is strongly associated with frailty, and several commonly prescribed
drugs are strongly associated with increased mortality. It must be considered that frail patients
need coordinated attention where the diverse specialist taking care of them jointly examines the
interactions between the diversity of treatments prescribed., The work in this paper has been partially supported by FEDER funds for the MICIN project PID2020-116346GB-I00, and 2016111138 of the health funding program of the Basque Government. The author M.G. has received research funds from the Basque Government as the head of the Grupo de Inteligencia Computacional, Universidad del Pais Vasco, UPV/EHU since 2007 until 2025. The current code for the grant is IT1689-22. Additionally, the authors are participating in Elkartek projects KK-2022/00051 and KK-2021/00070.




Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

RUC. Repositorio da Universidade da Coruña
  • Górriz, Juan M.
  • Álvarez-Illán, I.
  • Álvarez-Marquina, Agustín
  • Arco, Juan Eloy
  • Atzmueller, Martin
  • Ballarini, F.
  • Barakova, Emilia
  • Bologna, Guido
  • Duro, Richard J. Richard J. xxx
  • Santos Reyes, José
Financiado para publicación en acceso aberto: Universidad de Granada / CBUA., [Abstract]: Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9th International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications., Funding for open access charge: Universidad de Granada / CBUA. The work reported here has been partially funded by many public and private bodies: by the MCIN/AEI/10.13039/501100011033/ and FEDER “Una manera de hacer Europa” under the RTI2018-098913-B100 project, by the Consejeria de Economia, Innovacion, Ciencia y Empleo (Junta de Andalucia) and FEDER under CV20-45250, A-TIC-080-UGR18, B-TIC-586-UGR20 and P20-00525 projects, and by the Ministerio de Universidades under the FPU18/04902 grant given to C. Jimenez-Mesa, the Margarita-Salas grant to J.E. Arco, and the Juan de la Cierva grant to D. Castillo-Barnes.
This work was supported by projects PGC2018-098813-B-C32 & RTI2018-098913-B100 (Spanish “Ministerio de Ciencia, Innovacón y Universidades”), P18-RT-1624, UMA20-FEDERJA-086, CV20-45250, A-TIC-080-UGR18 and P20 00525 (Consejería de econnomía y conocimiento, Junta de Andalucía) and by European Regional Development Funds (ERDF). M.A. Formoso work was supported by Grant PRE2019-087350 funded by MCIN/AEI/10.13039/501100011033 by “ESF Investing in your future”. Work of J.E. Arco was supported by Ministerio de Universidades, Gobierno de España through grant “Margarita Salas”.
The work reported here has been partially funded by Grant PID2020-115220RB-C22 funded by MCIN/AEI/10.13039/501100011033 and, as appropriate, by “ERDF A way of making Europe”, by the “European Union” or by the “European Union NextGenerationEU/PRTR”.
The work of Paulo Novais is financed by National Funds through the Portuguese funding agency, FCT - Fundaça̋o para a Ciência e a Tecnologia within project DSAIPA/AI/0099/2019.
Ramiro Varela was supported by the Spanish State Agency for Research (AEI) grant PID2019-106263RB-I00.
José Santos was supported by the Xunta de Galicia and the European Union (European Regional Development Fund - Galicia 2014–2020 Program), with grants CITIC (ED431G 2019/01), GPC ED431B 2022/33, and by the Spanish Ministry of Science and Innovation (project PID2020-116201GB-I00). The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).
The work reported here has been partially funded by Project Fondecyt 1201572 (ANID).
In [247], the project has received funding by grant RTI2018-098969-B-100 from the Spanish Ministerio de Ciencia Innovación y Universidades and by grant PROMETEO/2019/119 from the Generalitat Valenciana (Spain). In [248], the research work has been partially supported by the National Science Fund of Bulgaria (scientific project “Digital Accessibility for People with Special Needs: Methodology, Conceptual Models and Innovative Ecosystems”), Grant Number KP-06-N42/4, 08.12.2020; EC for project CybSPEED, 777720, H2020-MSCA-RISE-2017 and OP Science and Education for Smart Growth (2014–2020) for project Competence Center “Intelligent mechatronic, eco- and energy saving sytems and technologies”BG05M2OP001-1.002-0023.
The work reported here has been partially funded by the support of MICIN project PID2020-116346GB-I00.
The work reported here has been partially funded by many public and private bodies: by MCIN/AEI/10.13039/501100011033 and “ERDF A way to make Europe” under the PID2020-115220RB-C21 and EQC2019-006063-P projects; by MCIN/AEI/10.13039/501100011033 and “ESF Investing in your future” under FPU16/03740 grant; by the CIBERSAM of the Instituto de Salud Carlos III; by MinCiencias project 1222-852-69927, contract 495-2020.
The work is partially supported by the Autonomous Government of Andalusia (Spain) under project UMA18-FEDERJA-084, project name Detection of anomalous behavior agents by DL in low-cost video surveillance intelligent systems. Authors gratefully acknowledge the support of NVIDIA Corporation with the donation of a RTX A6000 48 Gb.
This work was conducted in the context of the Horizon Europe project PRE-ACT, and it has received funding through the European Commission Horizon Europe Program (Grant Agreement number: 101057746). In addition, this work was supported by the Swiss State Secretariat for Education, Research and Innovation (SERI) under contract nummber 22 00058.
S.B Cho was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No. 2020-0-01361, Artificial Intelligence Graduate School Program (Yonsei University))., Junta de Andalucía; CV20-45250, Junta de Andalucía; A-TIC-080-UGR18, Junta de Andalucía; B-TIC-586-UGR20, Junta de Andalucía; P20-00525, Junta de Andalucía; P18-RT-1624, Junta de Andalucía; UMA20-FEDERJA-086, Portugal. Fundação para a Ciência e a Tecnologia; DSAIPA/AI/0099/2019, Xunta de Galicia; ED431G 2019/01, Xunta de Galicia; GPC ED431B 2022/33, Chile. Agencia Nacional de Investigación y Desarrollo; 1201572, Generalitat Valenciana; PROMETEO/2019/119, Bulgarian National Science Fund; KP-06-N42/4, Bulgaria. Operational Programme Science and Education for Smart Growth; BG05M2OP001-1.002-0023, Colombia. Ministerio de Ciencia, Tecnología e Innovación; 1222-852-69927, Junta de Andalucía; UMA18-FEDERJA-084, Suíza. State Secretariat for Education, Research and Innovation; 22 00058, Institute of Information & Communications Technology Planning & Evaluation (Corea del Sur); 2020-0-01361




Computational approaches to Explainable Artificial Intelligence: Advances in theory, applications and trends

RUA. Repositorio Institucional de la Universidad de Alicante
  • Górriz, J.M.
  • Álvarez-Illán, I.
  • Álvarez-Marquina, A.
  • Arco, J.E.
  • Atzmueller, M.
  • Ballarini, F.
  • Barakova, E.
  • Bologna, G.
  • Bonomini, P.
  • Castellanos-Dominguez, G.
  • Castillo-Barnes, D.
  • Cho, S.B.
  • Contreras, R.
  • Cuadra, J.M.
  • Domínguez, E.
  • Domínguez-Mateos, F.
  • Duro, R.J.
  • Elizondo, D.
  • Fernández-Caballero, A.
  • Fernandez-Jover, E.
  • Formoso, M.A.
  • Gallego-Molina, N.J.
  • Gamazo, J.
  • García González, J.
  • Garcia-Rodriguez, Jose
  • Garre, C.
  • Garrigós, J.
  • Gómez-Rodellar, A.
  • Gómez-Vilda, P.
  • Graña, M.
  • Guerrero-Rodriguez, Byron
  • Hendrikse, S.C.F.
  • Jimenez-Mesa, C.
  • Jodra-Chuan, M.
  • Julián, Vicente
  • Kotz, G.
  • Kutt, K.
  • Leming, M.
  • Lope, J. de
  • Macas, B.
  • Marrero-Aguiar, V.
  • Martinez, J.J.
  • Martinez-Murcia, F.J.
  • Martínez-Tomás, R.
  • Mekyska, J.
  • Nalepa, G.J.
  • Novais, Paulo
  • Orellana, D.
  • Ortiz, A.
  • Palacios-Alonso, Daniel
  • Palma, J.
  • Pereira, A.
  • Pinacho-Davidson, P.
  • Pinninghoff, M.A.
  • Ponticorvo, M.
  • Psarrou, A.
  • Ramírez, J.
  • Rincón, M.
  • Rodellar-Biarge, V.
  • Rodríguez-Rodríguez, I.
  • Roelofsma, P.H.M.P.
  • Santos, J.
  • Salas-Gonzalez, D.
  • Salcedo-Lagos, P.
  • Segovia, F.
  • Shoeibi, A.
  • Silva, M.
  • Simic, D.
  • Suckling, J.
  • Treur, J.
  • Tsanas, A.
  • Varela, R.
  • Wang, S.H.
  • Wang, W.
  • Zhang, Y.D.
  • Zhu, H.
  • Zhu, Z.
  • Ferrández-Vicente, J.M.
Deep Learning (DL), a groundbreaking branch of Machine Learning (ML), has emerged as a driving force in both theoretical and applied Artificial Intelligence (AI). DL algorithms, rooted in complex and non-linear artificial neural systems, excel at extracting high-level features from data. DL has demonstrated human-level performance in real-world tasks, including clinical diagnostics, and has unlocked solutions to previously intractable problems in virtual agent design, robotics, genomics, neuroimaging, computer vision, and industrial automation. In this paper, the most relevant advances from the last few years in Artificial Intelligence (AI) and several applications to neuroscience, neuroimaging, computer vision, and robotics are presented, reviewed and discussed. In this way, we summarize the state-of-the-art in AI methods, models and applications within a collection of works presented at the 9 International Conference on the Interplay between Natural and Artificial Computation (IWINAC). The works presented in this paper are excellent examples of new scientific discoveries made in laboratories that have successfully transitioned to real-life applications.




An ongoing review of speech emotion recognition

Archivo Digital UPM
  • Lope Asiaín, Javier de
  • Graña Romay, Manuel María
User emotional status recognition is becoming a key feature in advanced Human Computer Interfaces (HCI). A key source of emotional information is the spoken expression, which may be part of the interaction between the human and the machine. Speech emotion recognition (SER) is a very active area of research that involves the application of current machine learning and neural networks tools. This ongoing review covers recent and classical approaches to SER reported in the literature.