Random Forest as a tumour genetic marker extractor

Identifying tumour genetic markers is an essential task for biomedicine. In this thesis, we analyse a dataset of chromosomal rearrangements of cancer samples and present a methodology for extracting genetic markers from this dataset by using a Random Forest as a feature selection tool

Wordnet y Deep Learning: Una posible unión

Uno de los campos de estudio en alza del momento es el de Machine Learning, una rama de la inteligencia artificial que da a ciertos programas la habilidad de aprender autónoma-mente a partir de datos. Al no programar de forma directa los programas, sino entrenados, dentro de este campo una de las mayores incógnitas es descubrir por que algunos algoritmos toman ciertas decisiones en vez de otras. En este trabajo echaremos una ojeada dentro de una red convolucional profunda y estudiaremos las diferentes relaciones que se pueden encontrar entre una red convolucional entrenada con el conjunto de datos de imagenet y los synsets de wordnet. Todo esto basándonos en el trabajo presentado en el artículo An Out-of-the-box Full-network Embedding for Convolutional Neural Networks

Higher frequency of comorbidities in fully vaccinated patients admitted to the ICU due to severe COVID-19: a prospective, multicentre, observational study

Severe COVID-19 disease requiring ICU admission is possible in the fully vaccinated population, especially in those with immunocompromised status and other comorbidities. Interventions to improve vaccine response might be necessary in this population.Peer ReviewedArticle signat per 23 autors/es: Anna Motos, Alexandre López-Gavín, Jordi Riera, Adrián Ceccato, Laia Fernández-Barat, Jesús F. Bermejo-Martin, Ricard Ferrer, David de Gonzalo-Calvo, Rosario Menéndez, Raquel Pérez-Arnal, Dario García-Gasulla, Alejandro Rodriguez, Oscar Peñuelas, José Ángel Lorente, Raquel Almansa, Albert Gabarrus, Judith Marin-Corral, Pilar Ricart, Ferran Roche-Campo, Susana Sancho Chinesta, Lorenzo Socias, Ferran Barbé, Antoni Torres on behalf of the CIBERESUCICOVID Project (COV20/00110, ISCIII).Postprint (published version

ICU-acquired pneumonia is associated with poor health post-COVID-19 syndrome

Some patients previously presenting with COVID-19 have been reported to develop persistent COVID-19 symptoms. While this information has been adequately recognised and extensively published with respect to non-critically ill patients, less is known about the incidence and factors associated with the characteristics of persistent COVID-19. On the other hand, these patients very often have intensive care unit-acquired pneumonia (ICUAP). A second infectious hit after COVID increases the length of ICU stay and mechanical ventilation and could have an influence on poor health post-COVID 19 syndrome in ICU-discharged patients. Methods: This prospective, multicentre, and observational study was carrid out across 40 selected ICUs in Spain. Consecutive patients with COVID-19 requiring ICU admission were recruited and evaluated three months after hospital discharge. Results: A total of 1255 ICU patients were scheduled to be followed up at 3 months; however, the final cohort comprised 991 (78.9%) patients. A total of 315 patients developed ICUAP (97% of them had ventilated ICUAP). Patients requiring invasive mechanical ventilation had more persistent post-COVID-19 symptoms than those who did not require mechanical ventilation. Female sex, duration of ICU stay, development of ICUAP, and ARDS were independent factors for persistent poor health post-COVID-19. Conclusions: Persistent post-COVID-19 symptoms occurred in more than two-thirds of patients. Female sex, duration of ICU stay, development of ICUAP, and ARDS all comprised independent factors for persistent poor health post-COVID-19. Prevention of ICUAP could have beneficial effects in poor health post-COVID-19.Financial support was provided by the Instituto Carlos III de Madrid (COV20/00110, ISCIII) and by the Centro de Investigación Biomedica En Red—Enfermedades Respiratorias (CIBERES). DdGC has received financial support from Instituto de Salud Carlos III (Miguel Servet 2020: CP20/00041), co-funded by European Social Fund (ESF)/ “Investing in your future”Peer ReviewedArticle signat per 53 autors/es: Ignacio Martin-Loeches (1,2,3), Anna Motos (1,3), Rosario Menéndez (1,4), Albert Gabarrús (1,4), Jessica González (5,6), Laia Fernández-Barat (1,3), Adrián Ceccato (1,3), Raquel Pérez-Arnal (7), Dario García-Gasulla (7), Ricard Ferrer (1,8), Jordi Riera (1,8), José Ángel Lorente (1,9), Óscar Peñuelas (1,9), Jesús F. Bermejo-Martin (1,10,11), David de Gonzalo-Calvo (5,6), Alejandro Rodríguez (12), Ferran Barbé (5,6), Luciano Aguilera (13), Rosario Amaya-Villar (14), Carme Barberà (15), José Barberán (16), Aaron Blandino Ortiz (17), Elena Bustamante-Munguira (18), Jesús Caballero (19), Cristina Carbajales (20), Nieves Carbonell (21),Mercedes Catalán-González (22), Cristóbal Galbán (23), Víctor D. Gumucio-Sanguino (24), Maria del Carmen de la Torre (25), Emili Díaz (26), Elena Gallego (27), José Luis García Garmendia (28), José Garnacho-Montero (29), José M. Gómez (30), Ruth Noemí Jorge García (31), Ana Loza-Vázquez (32), Judith Marín-Corral (33), Amalia Martínez de la Gándara (34), Ignacio Martínez Varela (35), Juan Lopez Messa (36), Guillermo M. Albaiceta (37,38), Mariana Andrea Novo (39), Yhivian Peñasco (40), Pilar Ricart (41), Luis Urrelo-Cerrón (42), Angel Sánchez-Miralles (43), Susana Sancho Chinesta (44), Lorenzo Socias (45), Jordi Solé-Violan (1,46), Luis Tamayo Lomas (47), Pablo Vidal (48) and Antoni Torres (1,3)*, on behalf of CIBERESUCICOVID Project (COV20/00110 and ISCIII) // (1) CIBER of Respiratory Diseases (CIBERES), Institute of Health Carlos III, 28029 Madrid, Spain; (2) Pulmonary Department, Hospital Clinic, Universitat de Barcelona, IDIBAPS, 08036 Barcelona, Spain; (3) Department of Intensive Care Medicine, St. James’s Hospital, Multidisciplinary Intensive Care Research Organization (MICRO), James’s Street, D08 NHY1 Dublin, Ireland; (4) Pulmonary Department, University and Polytechnic Hospital La Fe, 46026 Valencia, Spain; (5) Translational Research in Respiratory Medicine Group (TRRM), Lleida Biomedical Research Institute (IRBLleida), 25198 Lleida, Spain; (6) Pulmonary Department, Hospital Universitari Arnau de Vilanova and Santa Maria, 25198 Lleida, Spain; (7) Barcelona Supercomputing Centre (BSC), 08034 Barcelona, Spain; (8) Intensive Care Department, Vall d’Hebron Hospital Universitari, SODIR Research Group, Vall d’Hebron Institut de Recerca (VHIR), 08035 Barcelona, Spain; (9) Hospital Universitario de Getafe, 28905 Madrid, Spain; (10) Hospital Universitario Río Hortega de Valladolid, 47012 Valladolid, Spain; (11) Instituto de Investigación Biomédica de Salamanca (IBSAL), Gerencia Regional de Salud de Castilla y León, 47007 Valladolid, Spain; (12) Critical Care Department, Hospital Joan XXIII, 43005 Tarragona, Spain; (13) Anestesia, Reanimación y Terapia del Dolor, Hospital Universitario de Basurto, 48013 Bilbao, Spain; (14) Intensive Care Clinical Unit, Hospital Universitario Virgen de Rocío, 41013 Sevilla, Spain; (15) Hospital Santa Maria, IRBLleida, 25198 Lleida, Spain; (16) Critical Care Department, Hospital Universitario HM Montepríncipe, Universidad San Pablo-CEU, 28660 Madrid, Spain; (17) Servicio de Medicina Intensiva, Hospital Universitario Ramón y Cajal, 28034 Madrid, Spain; (18) Department of Intensive Care Medicine, Hospital Clínico Universitario Valladolid, 47003 Valladolid, Spain; (19) Critical Care Department, Hospital Universitari Arnau de Vilanova, IRBLleida, 25198 Lleida, Spain; (20) Hospital Álvaro Cunqueiro, 36213 Vigo, Spain; (21) Intensive Care Unit, Hospital Clínico y Universitario de Valencia, 46010 Valencia, Spain; (22) Department of Intensive Care Medicine, Hospital Universitario 12 de Octubre, 28041 Madrid, Spain, (23) Department of Medicine, CHUS, Complejo Hospitalario Universitario de Santiago, 15076 Santiago de Compostela, Spain; (24) Department of Intensive Care, Hospital Universitari de Bellvitge, L’Hospitalet de Llobregat, 08907 Barcelona, Spain; (25) Hospital de Mataró de Barcelona, 08301 Mataró, Spain; (26) Department of Medicine, Universitat Autònoma de Barcelona (UAB), Critical Care Department, Corpo-Ració Sanitària Parc Taulí, Sabadell, 08208 Barcelona, Spain; (27) Unidad de Cuidados Intensivos, Hospital San Pedro de Alcántara, 10003 Cáceres, Spain; (28) Intensive Care Unit, Hospital San Juan de Dios del Aljarafe, 41930 Sevilla, Spain; (29) Intensive Care Clinical Unit, Hospital Universitario Virgen Macarena, 41009 Seville, Spain; (30) Hospital General Universitario Gregorio Marañón, 28009 Madrid, Spain; (31) Intensive Care Department, Hospital Nuestra Señora de Gracia, 50009 Zaragoza, Spain; (32) Unidad de Medicina Intensiva, Hospital Universitario Virgen de Valme, 41014 Sevilla, Spain; (33) Critical Care Department, Hospital del Mar-IMIM, 08003 Barcelona, Spain; (34) Department of Intensive Medicine, Hospital Universitario Infanta Leonor, 28031 Madrid, Spain; (35) Critical Care Department, Hospital Universitario Lucus Augusti, 27003 Lugo, Spain; (36) Critical Care Department, Complejo Asistencial Universitario de Palencia, 34005 Palencia, Spain; (37) Departamento de Biología Funcional, Instituto Universitario de Oncología del Principado de Asturias, Universidad de Oviedo, 33011 Oviedo, Spain; (38) Instituto de Investigación Sanitaria del Principado de Asturias, Hospital Central de Asturias, 33011 Oviedo, Spain; (39) Servei de Medicina Intensiva, Hospital Universitari Son Espases, Palma de Mallorca, 07120 Illes Balears, Spain; (40) Servicio de Medicina Intensiva, Hospital Universitario Marqués de Valdecilla, 39008 Santander, Spain; (41) Servei de Medicina Intensiva, Hospital Universitari Germans Trias, 08916 Badalona, Spain; (42) Hospital Verge de la Cinta, 08916 Tortosa, Spain; (43) Hospital de Sant Joan d’Alacant, 03550 Alacant, Spain; (44) Servicio de Medicina Intensiva, Hospital Universitario y Politécnico La Fe, 46026 Valencia, Spain; (45) Intensive Care Unit, Hospital Son Llàtzer, Palma de Mallorca, 07198 Illes Balears, Spain; (46) Critical Care Department, Hospital Dr. Negrín., 35019 Las Palmas de GC, Spain; (47) Critical Care Department, Hospital Universitario Río Hortega de Valladolid, 47102 Valladolid, Spain; (48) Intensive Care Unit, Complexo Hospitalario Universitario de Ourense, 32005 Ourense, Spain.Postprint (published version

Random Forest as a tumour genetic marker extractor

Identifying tumour genetic markers is an essential task for biomedicine. In this thesis, we analyse a dataset of chromosomal rearrangements of cancer samples and present a methodology for extracting genetic markers from this dataset by using a Random Forest as a feature selection tool

Large scale prediction of sick leave duration with nonlinear survival analysis algorithms

The management of sick leaves is a critical task that public and private health systems carry out. This enables the good care of sick workers and guarantees a safe return to their jobs. Most health systems enforce regulations that establish the duration of sick leave according to general rules for groups of diagnoses. However, these regulations do not account for the particularities of workers. On the one hand, an early return to work is sometimes possible, but this does not happen unless the worker pro-actively requests it. On the other hand, the worker’s health condition could demand for one or more leave extensions, but the system requires mandatory and sometimes unnecessary follow-ups, adding nuisance to patients and overhead to health systems. In both cases, the lack and excess of action by the health system represents extra costs for society. This paper proposes the analysis of a voluminous historical dataset of sick leaves (including medical and personal data) to predict the duration of future sick leaves for patients. The data mining process is performed for a large number of diagnoses to assess the possibility of using data driven models for broad decision-making. The nature and characteristics of the data makes it difficult to obtain models using classical methods, which is why the analysis focuses on non-linear machine learning-based survival analysis methods. In sight of the models performance, we move forward to its practical implementation, proposing a tool to manage the decision of what patients should be contacted at a given date using the predictions of the trained models. This tool will manage the whole cycle, continuously training on new data, performing daily predictions, and presenting the results to the health-care decision-maker for their assessment.This work has been funded by a collaboration between Asepeyo and BSC. We want to thank the following people from Asepeyo for their help and support that made this project possible. Xavier Calatrava Petisme, Alex Nogué Martinez, Enric Lleal Serra, Francisco Manuel Ventura Nofuentes, Oscar González Cherta, Francisco Sánchez Algarra, Eulàlia Borén and Vanessa Vegazo. We also want to thank to Nadia Tonello (BSC Data Manager) and Ulises Cortés (UPC/BSC) for their insightful comments.Peer ReviewedPostprint (author's final draft

Collection, processing and analysis of heterogeneous data coming from Spanish hospitals in the context of COVID-19

The COVID-19 pandemic has already caused more than 150,000,000 cases worldwide. In Spain this has lead to a massive and simultaneous saturation of all sanitary regions. Coherently, the quick and consistent understanding of the COVID-19 disease requires of the combined analysis of thousands of medical records generated by dozens of different institutions. In the context of the publicly funded CIBERES-UCI-COVID project, we have gathered, cleaned and preprocessed data from heterogeneous sources – more than 30 hospitals, with different data entry systems – in order to produce a unified database, of more than 6.000 patients, that is used in several clinical studies being carried by different multidisciplinary groups. In this paper, we identify the complexities we encountered, the solutions we applied, and we summarise the statistical and machine learning techniques we have applied for the studies.Peer ReviewedPostprint (published version

Comparative analysis of geolocation information through mobile-devices under different COVID-19 mobility restriction patterns in Spain

The COVID-19 pandemic is changing the world in unprecedented and unpredictable ways. Human mobility, being the greatest facilitator for the spread of the virus, is at the epicenter of this change. In order to study mobility under COVID-19, to evaluate the efficiency of mobility restriction policies, and to facilitate a better response to future crisis, we need to understand all possible mobility data sources at our disposal. Our work studies private mobility sources, gathered from mobile-phones and released by large technological companies. These data are of special interest because, unlike most public sources, it is focused on individuals rather than on transportation means. Furthermore, the sample of society they cover is large and representative. On the other hand, these data are not directly accessible for anonymity reasons. Thus, properly interpreting its patterns demands caution. Aware of that, we explore the behavior and inter-relations of private sources of mobility data in the context of Spain. This country represents a good experimental setting due to both its large and fast pandemic peak and its implementation of a sustained, generalized lockdown. Our work illustrates how a direct and naive comparison between sources can be misleading, as certain days (e.g., Sundays) exhibit a directly adverse behavior. After understanding their particularities, we find them to be partially correlated and, what is more important, complementary under a proper interpretation. Finally, we confirm that mobile-data can be used to evaluate the efficiency of implemented policies, detect changes in mobility trends, and provide insights into what new normality means in Spain.Part of this research has received funding from the European Union Horizon 2020 Programme under the SoBigData++ Project, grant agreement num. 871,042. Martí Català received funding from La Caixa Foundation (ID 100010434), under agreement LCF/PR/GN17/50300003; Martí Català received funding from Ministerio de Ciencia, Innovación y Universidades and FEDER, with the project PGC2018-095456-B-I00.E. A-L EAL thanks support from the Spanish Ministry of Science Innovation and Universities, (SAF2017-88019-C3-3R).Peer ReviewedPostprint (published version