Impact of Regressand Stratification in Dataset Shift Caused by Cross-Validation

Data that have not been modeled cannot be correctly predicted. Under this assumption, this research studies how k-fold cross-validation can introduce dataset shift in regression problems. This fact implies data distributions in the training and test sets to be different and, therefore, a deterioration of the model performance estimation. Even though the stratification of the output variable is widely used in the field of classification to reduce the impacts of dataset shift induced by cross-validation, its use in regression is not widespread in the literature. This paper analyzes the consequences for dataset shift of including different regressand stratification schemes in cross-validation with regression data. The results obtained show that these allow for creating more similar training and test sets, reducing the presence of dataset shift related to cross-validation. The bias and deviation of the performance estimation results obtained by regression algorithms are improved using the highest amounts of strata, as are the number of cross-validation repetitions necessary to obtain these better results.MCIU/AEI/ERDF, UE PGC2018098860-B-I00ERDF Operational Programme 2014-2020Economy and Knowledge Council of the Regional Government of Andalusia, Spain MCIN/AEI CEX2020-001105-M A-FQM-345-UGR1

COVID-19 and the Use of Angiotensin II Receptor Blockers in Older Chronic Hypertensive Patients: Systematic Review and Meta-Analysis

Angiotensin II-converting enzyme inhibitors (ACEIs) and selective angiotensin II receptor antagonists (ARAIIs) are widely used antihypertensive agents. Their use has generated controversy due to their possible influence on the health status of chronic patients infected with COVID-19. The objective of this work is to analyze the influence of COVID-19 on chronic hypertensive patients treated with ACEI and ARAII inhibitors. A systematic review and meta-analysis in the databases Pubmed, Pro-Quest and Scopus were carried out. The systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The search equation descriptors were obtained from the Medical Subject Headings (MeSH) thesaurus. The search equation was: “Older AND hypertension AND (COVID-19 OR coronavirus) AND primary care” and its equivalent in Spanish. Nineteen articles were obtained, with n = 10,806,159 subjects. Several studies describe the COVID-19 association with ACEI or ARAII treatment in hypertension patients as a protective factor, some as a risk factor, and others without a risk association. In the case of ACEI vs. ARAII, the risk described for the former has an odds ratio (OR) of 0.55, and for ARAII, an OR of 0.59. Some authors talk about mortality associated with COVID-19 and ACEI with a half ratio (HR) of 0.97, and also associated ARAIIs with an HR of 0.98. It is recommended to maintain the use of the renin–angiotensin–aldosterone axis in the context of the COVID-19 diseas

Principal component analysis - Practice 1.1

Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual de José Luis Romero Béjar y Carlos Francisco Salto Díaz. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente".This brief guide is intended to familiarize the reader with the following: Loading and installing R packages. Loading data sets of different formats from R base installation and from local directories. Basic descriptive statistics. Graphical utils from ggplot2 package. Deal with outliers: identification and making decisions. Principal component analysis: requirements, obtaining principal components, explained variance, appropriate number of principal components, graphical outputs, coordenates in the new reference system.Esta breve guía tiene como objetivo familiarizar al lector con lo siguiente: Carga e instalación de paquetes R. Carga de conjuntos de datos de diferentes formatos desde la instalación base de R y desde directorios locales. Estadística descriptiva básica. Utilidades gráficas del paquete ggplot2. Tratar con valores atípicos: identificación y toma de decisiones. Análisis de componentes principales: requisitos, obtención de componentes principales, varianza explicada, número adecuado de componentes principales, salidas gráficas, coordenadas en el nuevo sistema de referencia

Principal component analysis - Practice 1.2

Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual de José Luis Romero Béjar y Carlos Francisco Salto Díaz. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente".In this guide, a second example of dimensionality reduction in a dataset is performed using the R language. This brief guide is intended to familiarize the reader with the following: Exploratory data analisys to identify outliers and not available data (NA). Dealing with outliers: identification and decision making. Dealing with not available data (NA): identification and decision making. Principal component analysis: requirements, obtaining principal components, explained variance, appropriate number of principal components, graphical outputs, coordenates in the new reference system.En esta guía, se presenta un segundo ejemplo de reducción de dimensionalidad en un conjunto de datos utilizando el lenguaje R. Esta breve guía tiene como objetivo familiarizar al lector con lo siguiente: Análisis exploratorio de datos para identificar valores atípicos y datos no disponibles (NA). Manejo de valores atípicos: identificación y toma de decisiones. Manejo de datos no disponibles (NA): identificación y toma de decisiones. Análisis de componentes principales: requisitos, obtención de componentes principales, varianza explicada, número adecuado de componentes principales, salidas gráficas, coordenadas en el nuevo sistema de referencia

Factorial analysis - Practice 2

Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual de José Luis Romero Béjar y Carlos Francisco Salto Díaz. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente".En esta guía, de entre los 25 ítems de un test de personalidad, se identificarán las variables que corresponden a cada uno de los cinco aspectos de la personalidad de un individuo. Las cinco características que definen la personalidad de un individuo son: A - Amabilidad o simpatía; C - Conciencia o responsabilidad; E - Extraversión; N - Neuroticismo y - Apertura a las experiencias. Para ello, se considera el conjunto de datos bfi de la biblioteca psyh. Este conjunto de datos contiene 2800 observaciones con 28 variables, de las cuales 25 corresponden a los diferentes ítems de un test de personalidad. Esta breve guía pretende familiarizar al lector con lo siguiente: Realizar un análisis exploratorio previo de los datos para identificar posibles datos faltantes y valores extremos. Toma decisiones para datos faltantes y valores extremos. Verificar los supuestos y realizar un análisis factorial (FA). Elegir el número óptimo de factores. Interpretación de diferentes salidas gráficas de interés para este método. Lenguaje R: depuración de funciones.In this practice, from among 25 items of a personality test, the variables that correspond to each of the five aspects of the personality of an individual will be identified. The five characteristics that define the personality of an individual are: A - Agreeableness or friendliness; C - Consciousness or responsibility; E - Extraversion; N - Neuroticism and - Openness to experiences. To do this, the bfi data set from the psyh library is considered. This data set contains 2800 observations with 28 variables, of which 25 correspond to the different items of a personality test. This brief guide is intended to familiarize the reader with the following: Perform a prior exploratory analysis of the data to identify possible missing data and extreme values. Make decisions and deal with missing data and extreme values. Check the assumptions and perform a factorial analysis (FA). Choosing the optimal number of factors. Interpretation of different graphical outputs of interest for this method. R language: functions debugging

Decision-Tree-Based Approach for Pressure Ulcer Risk Assessment in Immobilized Patients

Applications where data mining tools are used in the fields of medicine and nursing are becoming more and more frequent. Among them, decision trees have been applied to different health data, such as those associated with pressure ulcers. Pressure ulcers represent a health problem with a significant impact on the morbidity and mortality of immobilized patients and on the quality of life of affected people and their families. Nurses provide comprehensive care to immobilized patients. This fact results in an increased workload that can be a risk factor for the development of serious health problems. Healthcare work with evidence-based practice with an objective criterion for a nursing professional is an essential addition for the application of preventive measures. In this work, two ways for conducting a pressure ulcer risk assessment based on a decision tree approach are provided. The first way is based on the activity and mobility characteristics of the Braden scale, whilst the second way is based on the activity, mobility and skin moisture characteristics. The results provided in this study endow nursing professionals with a foundation in relation to the use of their experience and objective criteria for quick decision making regarding the risk of a patient to develop a pressure ulcer.Consejeria de Salud, Junta de Andalucia (Fundacion Publica Andaluza Progreso y Salud) AP-0086-201

Análisis factorial - Práctica 2

Todo el material para el conjunto de actividades de este curso ha sido elaborado y es propiedad intelectual de José Luis Romero Béjar y Carlos Francisco Salto Díaz. Todos los materiales están protegidos por la Licencia Creative Commons CC BY-NC-ND que permite "descargar las obras y compartirlas con otras personas, siempre que se reconozca su autoría, pero no se pueden cambiar de ninguna manera ni se pueden utilizar comercialmente".En esta guía, de entre los 25 ítems de un test de personalidad, se identificarán las variables que corresponden a cada uno de los cinco aspectos de la personalidad de un individuo. Las cinco características que definen la personalidad de un individuo son: A - Amabilidad o simpatía; C - Conciencia o responsabilidad; E - Extraversión; N - Neuroticismo y - Apertura a las experiencias. Para ello, se considera el conjunto de datos bfi de la biblioteca psyh. Este conjunto de datos contiene 2800 observaciones con 28 variables, de las cuales 25 corresponden a los diferentes ítems de un test de personalidad. Esta breve guía pretende familiarizar al lector con lo siguiente: Realizar un análisis exploratorio previo de los datos para identificar posibles datos faltantes y valores extremos. Toma decisiones para datos faltantes y valores extremos. Verificar los supuestos y realizar un análisis factorial (FA). Elegir el número óptimo de factores. Interpretación de diferentes salidas gráficas de interés para este método. Lenguaje R: depuración de funciones.In this guide, among the 25 items of a personality test, the variables that correspond to each of the five aspects of an individual's personality will be identified. The five characteristics that define the personality of an individual are: A - Kindness or friendliness; C - Consciousness or responsibility; E - Extraversion; N - Neuroticism and - Openness to experiences. For this, the bfi data set from the psyh library is considered. This data set contains 2800 observations with 28 variables, of which 25 correspond to the different items of a personality test. This brief guide aims to familiarize the reader with the following: Perform a preliminary exploratory analysis of the data to identify possible missing data and extreme values. Make decisions for missing data and outliers. Check the assumptions and perform a factor analysis (FA). Choose the optimal number of factors. Interpretation of different graphic outputs of interest for this method. R language: debugging functions

Pressure Ulcers Risk Assessment According to Nursing Criteria

Pressure ulcers (PU) represent a health problem with a significant impact on the morbidity and mortality of immobilized patients, and on the quality of life of affected people and their families. Risk assessment of pressure ulcers incidence must be carried out in a structured and comprehensive manner. The Braden Scale is the result of an analysis of risk factors that includes subscales that define exactly what should be interpreted in each one. The healthcare work with evidence-based practice with an objective criterion by the nursing professional is an essential addition for the application of preventive measures. Explanatory models based on the different subscales of Braden Scale purvey an estimation to level changes in the risk of suffering PU. A binary-response logistic regression model, supported by a study with an analytical, observational, longitudinal, and prospective design in the Granada-Metropolitan Primary Healthcare District (DSGM) in Andalusia (Southern Spain), with a sample of 16,215 immobilized status patients, using a Braden Scale log, is performed. A model that includes the mobility and activity scales achieves a correct classification rate of 86% (sensitivity (S) = 87.57%, specificity (SP) = 81.69%, positive predictive value (PPV) = 91.78%, and negative preventive value (NPV) = 73.78%), while if we add the skin moisture subscale to this model, the correct classification rate is 96% (S = 90.74%, SP = 88.83%, PPV = 95.00%, and NPV = 80.42%). The six subscales provide a model with a 99.5% correct classification rate (S = 99.93%, SP = 98.50%, PPV = 99.36%, and NPV = 99.83%). This analysis provides useful information to help predict this risk in this group of patients through objective nursing criteria.Consejeria de Salud, Junta de Andalucia (Fundacion Publica Andaluza Progreso y Salud) AP-0086-201

Telemedicine in Elderly Hypertensive and Patients with Chronic Diseases during the COVID-19 Pandemic: A Systematic Review and Meta-Analysis

Background: One aspect of the distancing measures imposed in response to the COVID-19 pandemic is that telemedicine consultations have increased exponentially. Among these consultations, the assessment and follow-up of patients with chronic diseases in a non-presential setting has been strengthened considerably. Nevertheless, some controversy remains about the most suitable means of patient follow-up. Objective: To analyze the impact of the telemedicine measures implemented during the COVID-19 period on chronic patients. Material and Methods: A systematic review was carried out using the following databases: PubMed, Pro-Quest, and Scopus. The systematic review followed the guidelines outlined in the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The search equation utilized descriptors sourced from the Medical Subject Headings (MeSH) thesaurus. The search equation was: “hypertension AND older AND primary care AND (COVID-19 OR coronavirus)” and its Spanish equivalent. Results: The following data were obtained: 14 articles provided data on 6,109,628 patients and another 4 articles focused on a study population of 9684 physicians. Telemedicine was less likely to be used by elderly patients (OR 0.85; 95% C.I. 0.83–0.88; p = 0.05), those of Asian race (OR 0.69; 95% C.I. 0.66–0.73; p = 0.05), and those whose native language was not English (OR 0.89; 95% C.I. 0.78–0.9; p = 0.05). In primary care, lower use of telemedicine was associated with residents of rural areas (OR 0.81; p = 0.05), patients of African American race (OR 0.65, p = 0.05), and others (OR 0.64; p = 0.05). A high proportion (40%) of physicians had no prior training in telemedicine techniques. The highest quality in terms of telephone consultation was significantly associated with physicians who did not increase their prescription of antibiotherapy during the pandemic (OR = 0.30, p = 0.05) or prescribe more tests (OR 0.06 p = 0.05), i.e., who maintained their former clinical criteria despite COVID-19. Conclusions: Telemedicine is of proven value and has been especially useful in the COVID-19 pandemic. A mixed remote–presential model is most efficient. Appropriate training in this area for physicians and patients, together with correct provision, is essential to prevent errors in implementation and us

Explanatory Models of Burnout Diagnosis Based on Personality Factors in Primary Care Nurses

Burnout in the primary care service takes place when there is a high level of interaction between nurses and patients. Explanatory models based on psychological and personality related variables provide an approximation to level changes in the three dimensions of the burnout syndrome. A categorical-response ordinal logistic regression model, based on a quantitative, crosscutting, multicentre, descriptive study with 242 primary care nurses in the Andalusian Health Service in Granada (Spain) is performed for each dimension. The three models included all the variables related to personality. The risk factor friendliness was significant at population level for the three dimensions, whilst openness was never significant. Neuroticism was significant in the models related to emotional exhaustion and depersonalization, whilst responsibility was significant for the models referred to depersonalization and personal accomplishment dimensions. Finally, extraversion was also significant in the emotional exhaustion and personal accomplishment dimensions. The analysis performed provides useful information, making more readily the diagnosis and evolution of the burnout syndrome in this collective.Junta de Andalucia P20_0062