Sample selection via clustering to construct support vector-like classifiers

This paper explores the possibility of constructing RBF classifiers which, somewhat like support vector machines, use a reduced number of samples as centroids, by means of selecting samples in a direct way. Because sample selection is viewed as a hard computational problem, this selection is done after a previous vector quantization: this way obtaining also other similar machines using centroids selected from those that are learned in a supervised manner. Several forms of designing these machines are considered, in particular with respect to sample selection; as well as some different criteria to train them. Simulation results for well-known classification problems show very good performance of the corresponding designs, improving that of support vector machines and reducing substantially their number of units. This shows that our interest in selecting samples (or centroids) in an efficient manner is justified. Many new research avenues appear from these experiments and discussions, as suggested in our conclusions.Publicad

Dimensionality reduction and ensemble of LSTMs for antimicrobial resistance prediction

Bacterial resistance to antibiotics has been rapidly increasing, resulting in a low antibiotic effectiveness even treating common infections. The presence of resistant pathogens in environments such as a hospital Intensive Care Unit (ICU) exacerbates the critical admission-acquired infections. This work focuses on the prediction of antibiotic resistance in Pseudomonas aeruginosa nosocomial infections at the ICU, using Long Short-Term Memory (LSTM) artificial neural networks as predictive method. The analyzed data were extracted from the Electronic Health Records (EHR) of patients admitted in the University Hospital of Fuenlabrada from 2004 to 2019, and were modeled as Multivariate Time Series. A data-driven dimensionality reduction method is built by adapting three feature importance techniques from the literature to the considered data, and proposing an algorithm for selecting the most appropriate number of features. This is done using LSTM sequential capabilities so that the temporal aspect of features is taken into account. Furthermore, an ensemble of LSTMs is used to reduce the performance variance. Our results indicate that the patient's admission information, the antibiotics administered during the ICU stay, and the previous antimicrobial resistance are the most important features. The proposed dimensionality reduction method dramatically reduces the number of features while considerably increasing the prediction performance. The variance in the performance is reduced by considering the ensemble of classifiers. In essence, the proposed framework achieve, in a computationally cost efficient manner, promising results for supporting decisions in this clinical task, characterized by high dimensionality, data scarcity and concept drift.This work has been partly supported by the Spanish Research Agency, grant numbers PID2019-106623RB-C41, AEI/10.13039/501100011033 (BigTheory), PID2019-107768RA-I00 (AAVis-BMR), by funding action by the Community of Madrid in the framework of the Multiannual Agreement with Rey Juan Carlos University in line of action 1 ‘‘Encouragement of Young Phd students investigation’’ Project Mapping-UCI (Ref F661), by the IDEAI-UPC Consolidated Research Group Grant from Catalan Agency of University and Research Grants (AGAUR, Generalitat de Catalunya) (2017 SGR 574) and by the Secretariat for Universities and Research of the Ministry of Research and Universities of the Government of Catalonia and the European Social Fund (2021 FI-B 00965).Peer ReviewedPostprint (published version

Antimicrobial Resistance Prediction in Intensive Care Unit for Pseudomonas Aeruginosa using Temporal Data-Driven Models

One threatening medical problem for human beings is the increasing antimicrobial resistance of some microorganisms. This problem is especially difficult in Intensive Care Units (ICUs) of hospitals due to the vulnerable state of patients. Knowing in advance whether a concrete bacterium is resistant or susceptible to an antibiotic is a crux step for clinicians to determine an effective antibiotic treatment. This usual clinical procedure takes approximately 48 hours and it is named antibiogram. It tests the bacterium resistance to one or more antimicrobial families (six of them considered in this work). This article focuses on cultures of the Pseudomonas Aeruginosa bacterium because is one of the most dangerous in the ICU. Several temporal data-driven models are proposed and analyzed to predict the resistance or susceptibility to a determined antibiotic family previously to know the antibiogram result and only using the available past information from a data set. This data set is formed by anonymized electronic health records data from more than 3300 ICU patients during 15 years. Several data-driven classifier methods are used in combination with several temporal modeling approaches. The results show that our predictions are reasonably accurate for some antimicrobial families, and could be used by clinicians to determine the best antibiotic therapy in advance. This early prediction can save valuable time to start the adequate treatment for an ICU patient. This study corroborates the results of a previous work pointing that the antimicrobial resistance of bacteria in the ICU is related to other recent resistance tests of ICU patients. This information is very valuable for making accurate antimicrobial resistance predictions

Editor's Note

Artificial Intelligence has become nowadays one of the main relevant technologies that is driven us to a new revolution, a change in society, just as well as other human inventions, such as navigation, steam machines, or electricity did in our past. There are several ways in which AI might be developed, and the European Union has chosen a path, a way to transit through this revolution, in which Artificial Intelligence will be a tool at the service of Humanity. That was precisely the motto of the 2020 European Conference on Artificial Intelligence (“Paving the way towards Human-Centric AI”), of which these special issue is a selection of the best papers selected by the organizers of some of the Workshops in ECAI 2020

Influence of Attitudes toward Violence and Motor Impulsiveness on the Violent Behavior of Adolescents at School

Background: School violence during adolescence has become a major issue worldwide. Both impulsiveness and adolescents attitudes toward violence will influence violent behavior against peers at school. Our objective is to study the influence of motor impulsiveness and attitudes on adolescents violent behavior at school, as well as to assess sex and age differences. Methods: Cluster sampling was performed, obtaining a sample of 513 adolescents between 13 and 19 years from four centers of secondary education. Results: A strong relationship is found between violent school behavior, defined as relational and overt aggression, and attitudes towards violence perceived as legitimate defense and violence used to cope with problems and social relations. The results showed significant sex differences favoring the boys in all the variables studied, except for motor impulsiveness and relational aggression. In terms of age, we found significant differences only for motor impulsiveness, favoring the older age group (15 years). The current findings may provide an important core of evidence to support forensic decision making in pre-trial and court settings, and further contribute to recidivism prevention

Dispositivos lingüísticos de acogida, aprendizaje expansivo e interculturalidad: Contribuciones para la inclusión educativa de estudiantes extranjeros

Indexación: Scopus.In the context of the progressive increase of foreigners in public schools in Chile and the recent changes in migratory patterns, the presence of students who do not master the working language of the educational system has systematically incremented in recent years. In a scenario marked by the absence of educational policies that respond to this new linguistic diversity, the purpose of our research was to understand how schools have responded to the arrival of foreign students and what educational practices they have implemented to tackle this challenge. Through a two-year school ethnography in four public schools with high enrollment of foreigners, institutional documents were analyzed and field observation, interviews and focus groups were conducted with different actors amidst the educational communities. Through an analysis of thematic content, the results reveal the implementation of ten devices to manage educational responses to foreign students. We have analyzed three of these devices with a focus on linguistics based on the tensions and contradictions that occur within the educational institutions of which they are part, identifying significant elements and dimensions to drive forward expansive learning. Finally, we discuss these findings in light of the advances and challenges of the intercultural approach in our Latin American region.https://www.scielo.br/j/ep/a/NhL67jbv7H7VpZtQYjtHVFt/?lang=e

Manual de simulación clínica en especialidades médicas

Manual sobre técnicas y modos de simulación clínica en diversas especialidades médicas.La enseñanza y formación en medicina necesita el uso de la simulación. Existen evidencias de su uso desde hace cientos de años, pero, en los últimos años se ha incrementado y diseminado. La simulación clínica está validada científicamente en múltiples contextos médicos y de otras áreas profesionales de la salud. Y es considerada de gran importancia como proceso de entrenamiento y de mejora de las competencias y adquisición de habilidades médicas en campos que incluye desde la historia clínica, comunicación con el paciente, exploración, diagnóstico terapéutica médica-farmacológica y quirúrgica y seguridad al tratar al paciente. Hoy en día, para muchas técnicas y situaciones clínicas es inaceptable llegar junto a los pacientes sin un dominio adquirido en simulación. La simulación puede ocurrir sin el uso de recursos adicionales, solo las personas, o utilizando pocos o muchos recursos de baja hasta alta tecnología y se puede adaptar a los recursos disponibles, abarcando todas las áreas de conocimiento, y dentro de ellas competencias técnicas o actitudes, solas o en conjunto. El uso racional y basado en evidencia de la simulación es de la mayor importancia por la necesidad de una mayor efectividad y eficiencia en la transformación de los profesionales de la salud para que puedan mejorar su capacidad de atender a los pacientes. La simulación es también una buena herramienta de evaluación de competencias y habilidades en Medicina y otras disciplinas de las Ciencias de la Salud Este manual incluye técnicas y modos de simulación clínica en diversas especialidades médicas, útiles, para quien busque un manual práctico y actualizado.Cátedra de Mecenazgo de la Universidad de Málaga. Cátedra de Terapias Avanzadas en Patología Cardiovascular Cátedra de Mecenazgo de la Universidad de Málaga. Cátedra de Investigación Biomédica Quirón Salu

Treatment with tocilizumab or corticosteroids for COVID-19 patients with hyperinflammatory state: a multicentre cohort study (SAM-COVID-19)

Objectives: The objective of this study was to estimate the association between tocilizumab or corticosteroids and the risk of intubation or death in patients with coronavirus disease 19 (COVID-19) with a hyperinflammatory state according to clinical and laboratory parameters. Methods: A cohort study was performed in 60 Spanish hospitals including 778 patients with COVID-19 and clinical and laboratory data indicative of a hyperinflammatory state. Treatment was mainly with tocilizumab, an intermediate-high dose of corticosteroids (IHDC), a pulse dose of corticosteroids (PDC), combination therapy, or no treatment. Primary outcome was intubation or death; follow-up was 21 days. Propensity score-adjusted estimations using Cox regression (logistic regression if needed) were calculated. Propensity scores were used as confounders, matching variables and for the inverse probability of treatment weights (IPTWs). Results: In all, 88, 117, 78 and 151 patients treated with tocilizumab, IHDC, PDC, and combination therapy, respectively, were compared with 344 untreated patients. The primary endpoint occurred in 10 (11.4%), 27 (23.1%), 12 (15.4%), 40 (25.6%) and 69 (21.1%), respectively. The IPTW-based hazard ratios (odds ratio for combination therapy) for the primary endpoint were 0.32 (95%CI 0.22-0.47; p < 0.001) for tocilizumab, 0.82 (0.71-1.30; p 0.82) for IHDC, 0.61 (0.43-0.86; p 0.006) for PDC, and 1.17 (0.86-1.58; p 0.30) for combination therapy. Other applications of the propensity score provided similar results, but were not significant for PDC. Tocilizumab was also associated with lower hazard of death alone in IPTW analysis (0.07; 0.02-0.17; p < 0.001). Conclusions: Tocilizumab might be useful in COVID-19 patients with a hyperinflammatory state and should be prioritized for randomized trials in this situatio

The evolution of the ventilatory ratio is a prognostic factor in mechanically ventilated COVID-19 ARDS patients

Background: Mortality due to COVID-19 is high, especially in patients requiring mechanical ventilation. The purpose of the study is to investigate associations between mortality and variables measured during the first three days of mechanical ventilation in patients with COVID-19 intubated at ICU admission. Methods: Multicenter, observational, cohort study includes consecutive patients with COVID-19 admitted to 44 Spanish ICUs between February 25 and July 31, 2020, who required intubation at ICU admission and mechanical ventilation for more than three days. We collected demographic and clinical data prior to admission; information about clinical evolution at days 1 and 3 of mechanical ventilation; and outcomes. Results: Of the 2,095 patients with COVID-19 admitted to the ICU, 1,118 (53.3%) were intubated at day 1 and remained under mechanical ventilation at day three. From days 1 to 3, PaO2/FiO2 increased from 115.6 [80.0-171.2] to 180.0 [135.4-227.9] mmHg and the ventilatory ratio from 1.73 [1.33-2.25] to 1.96 [1.61-2.40]. In-hospital mortality was 38.7%. A higher increase between ICU admission and day 3 in the ventilatory ratio (OR 1.04 [CI 1.01-1.07], p = 0.030) and creatinine levels (OR 1.05 [CI 1.01-1.09], p = 0.005) and a lower increase in platelet counts (OR 0.96 [CI 0.93-1.00], p = 0.037) were independently associated with a higher risk of death. No association between mortality and the PaO2/FiO2 variation was observed (OR 0.99 [CI 0.95 to 1.02], p = 0.47). Conclusions: Higher ventilatory ratio and its increase at day 3 is associated with mortality in patients with COVID-19 receiving mechanical ventilation at ICU admission. No association was found in the PaO2/FiO2 variation