Modelo de predicción subespacial: Regresión Multivariante Gaussiana Subespacial

[ES]El objetivo de esta tesis doctoral es contribuir al desarrollo de nuevos métodos que permitan mejorar los modelos clásicos de regresión. Los modelos de regresión clásicos buscan la manera de hallar una variable respuesta en función de un conjunto de variables independientes. Estas técnicas persiguen un objetivo en concreto, predecir la variable dependiente. Esta situación provoca que las variables independientes con las que construimos nuestro modelo cobren una gran importancia. El modelo construido necesita estas variables de entra- da para hallar la variable o variables (regresión lineal multivariante) de salida. La relación entre ambos conjuntos de variables tiene una única dirección. En el caso de querer cambiar variables de salida por variables de entrada o viceversa necesitamos construir nuevos modelos. Estas limitaciones provocan que los procedimientos sean rígidos. Su adaptabilidad a diferentes situaciones es susceptible a cambios en su estructura original, lo que provoca nuevos cálculos computacionales que hacen más complejo el desarrollo. Una de las motivaciones principales de esta tesis es la búsqueda de un modelo que rompa con esta rigidez permitiendo a las variables tener diferentes roles sin por ello perder poder predictivo. Para poder lograr nuestro objetivo hemos asociado dos campos estadísticos de diversa índole, técnicas de reducción dimensional y procesos gaussianos. Las técnicas de reducción dimensional nos permiten, entre otras cosas, conocer mejor la estructura de nuestros datos, simplificar métodos complejos o reducir la multicolinealidad. Por otro lado, los procesos gaussianos multivariantes son capaces de calcular un conjunto de variables correlacionadas por un dominio continuo (espacio o tiempo principalmente). Las técnicas de reducción dimensional son en su mayoría métodos exploratorios que permiten describir de forma intrínseca datos multivariantes. Estos procedimientos reproducen sobre planos factoriales hipotéticos nuestros datos en función de las variables que los representan. Aunque estas técnicas están muy presentes en el análisis multivariante, su poder predictivo es bajo. Los procesos gaussianos son modelos estadísticos en los que las observaciones suceden en un dominio continuo como espacio o tiempo. El caso espacial es descrito por las técnicas de krigeaje. Estos métodos de interpolación basan su poder de predicción en la denominada covarianza espacial y/o temporal y la distribución normal de sus variables. La idea básica de estas técnicas es predecir los valores en un punto desconocido del espacio calculando un promedio ponderado de los valores cercanos conocidos. El modelo que desarrollamos “Regresión Multivariante Gaussiana Subespacial” (MGSR) conjuga ambas corrientes estadísticas. Partiendo de unas coordenadas subespaciales generadas por una técnica cualquiera de reducción dimensional y asociando a éstas sus valores reales, podemos construir una nueva matriz sobre la cual aplicar un proceso gaussiano como el cokriging (kriging multivariante). Este proceso nos permite adivinar múltiples combinaciones entre las variables analizadas y a partir de ellas construir nuestros modelos predictivos

Application of Artificial Intelligence Algorithms Within the Medical Context for Non-Specialized Users: the CARTIER-IA Platform

The use of advanced algorithms and models such as Machine Learning, Deep Learning and other related approaches of Artificial Intelligence have grown in their use given their benefits in different contexts. One of these contexts is the medical domain, as these algorithms can support disease detection, image segmentation and other multiple tasks. However, it is necessary to organize and arrange the different data resources involved in these scenarios and tackle the heterogeneity of data sources. This work presents the CARTIER-IA platform: a platform for the management of medical data and imaging. The goal of this project focuses on providing a friendly and usable interface to organize structured data, to visualize and edit medical images, and to apply Artificial Intelligence algorithms on the stored resources. One of the challenges of the platform design is to ease these complex tasks in a way that non-AI-specialized users could benefit from the application of AI algorithms without further training. Two use cases of AI application within the platform are provided, as well as a heuristic evaluation to assess the usability of the first version of CARTIER-IA. Year of Publication 2021 Journal International Journal of Interactive Multimedia and Artificial Intelligence Volume 6 Issue Regular Issue Number 6 Number of Pages 46-53 Date Published 06/2021 ISSN Number 1989-1660 URL https://www.ijimai.org/journal/sites/default/files/2021-05/ijimai_6_6_5.pdf DOI 10.9781/ijimai.2021.05.005 DOI Google Scholar BibTeX EndNote X3 XML EndNote 7 XML Endnote tagged Marc RIS Attachment ijimai_6_6_5.pdf 932.11 K

Are Textual Recommendations Enough? Guiding Physicians Toward the Design of Machine Learning Pipelines Through a Visual Platform

The prevalence of artificial intelligence (AI) in our daily lives is often exaggerated by the media, leading to a positive public perception while overlooking potential problems. In the field of medicine, it is crucial to educate future healthcare professionals on the advantages and disadvantages of AI and to emphasize the importance of creating fair, ethical, and reproducible models. The KoopaML platform was developed to provide an educational and user-friendly interface for inexperienced users to create AI pipelines. This study analyzes the quantitative and interaction data gathered from a usability test involving physicians from the University Hospital of Salamanca, with the aim of identifying new interaction paradigms to improve the platform’s usability. The results shown that the platform is difficult to learn for inexperienced users due to its contents related to AI. Following these results, a set of improvements are proposed for the next version of KoopaML, focusing on reducing the interactions needed to create the pipelines

KoopaML, a Machine Learning platform for medical data analysis

Machine Learning allows facing complex tasks related to data analysis with big datasets. This Artificial Intelligence branch allows not technical contexts to get benefits related to data processing and analysis. In particular, in medicine, medical professionals are increasingly interested in Machine Learning to identify patterns in clinical cases and make predictions regarding health issues. However, many do not have the necessary programming or technological skills to perform these tasks. Many different tools focus on developing Machine Learning pipelines, from libraries for developers and data scientists to visual tools for experts or platforms to learn. However, we have identified some requirements in the medical context that raise the need to create a customized platform adapted to end-user found in this context. This work describes the design process and the first version of KoopaML, an ML platform to bridge the data science gaps of physicians while automatizing Machine Learning pipelines. The platform is focused on enhanced interactivity to improve the engagement of physicians while still providing all the benefits derived from the introduction of Machine Learning pipelines in medical departments, as well as integrated ongoing training during the use of the tool’s features

Flexible Heuristics for Supporting Recommendations Within an AI Platform Aimed at Non-expert Users

The use of Machine Learning (ML) to resolve complex tasks has become popular in several contexts. While these approaches are very effective and have many related benefits, they are still very tricky for the general audience. In this sense, expert knowledge is crucial to apply ML algorithms properly and to avoid potential issues. However, in some situations, it is not possible to rely on experts to guide the development of ML pipelines. To tackle this issue, we present an approach to provide customized heuristics and recommendations through a graphical platform to build ML pipelines, namely KoopaML, focused on the medical domain.With this approach, we aim not only at providing an easy way to apply ML for non-expert users, but also at providing a learning experience for them to understand how these methods work

Conociendo un estilo de vida espiritual de Perú

The objective of the article is to determine the characteristics of a spiritual lifestyle in Peru. The type of research is qualitative, descriptive, cross-sectional. The procedures included a population of 236 guests, of which 112 were older adults (over 60 years old), 99 young people (up to 30 years old) and 25 adults (between 30 and 60 years old) and finally there were 50 people from Peru who sent your responses or comments. The results indicate that there are no significant differences between gender and preference towards a natural recreation center that offers C-M-E balance; However, mathematically, the majority prefers a place with natural furniture. There are also no significant differences between gender and geographic location of the recreation center in "La Mariposa Town Center"; to half it seems indifferent and to the other half it seems affordable, and the aforementioned place has the following characteristics: it is far from the city, near the Piura-Peru river, away from noise, and surrounded by a natural environment. There are also no significant differences between gender and amount of money spent on the last illness, since half spent less than S/ 1,200.00 (less than $300.00) and the other half spent more than the previous figure. There are also no significant differences between gender and amount of money available for 1 day in the recreation center to balance C-M-E; since half are willing to spend less than S/ 360.00 (less than$120.00) and the other half more than S/ 360.00 (more than $120.00). There are also no significant differences between age and amount of money available for 1 day in the recreation center to balance C-M-E, since half are willing to spend less than S/ 480.00 (less than$ 120.00) and the other half more than S/ 480.00 (over $120.00). There are also no significant differences between gender and willingness to request the services of a recreation center to balance C-M-E, since the majority would definitely request such services. The conclusions are: (1) The C-M-E balance program generates positive effects with a better quality of life, moving people away from the condition of vulnerability in the face of future pandemics. (2) It is recommended that the managers of private organizations manage the construction of recreation centers where the C-M-E balance program is applied, favoring their workers and their clients. (3) In the same way, it is recommended that officials of government organizations request the services of recreation centers that offer the C-M-E balance program to collaborate with their workers and users in achieving well-being.El objetivo del artículo es determinar las características de un estilo de vida espiritual de Perú. El tipo de investigación es cualitativa, descriptiva, transversal. Los procedimientos incluyeron una población de 236 invitados, de los cuales 112 fueron adultos mayores (mayores de 60 años), 99 jóvenes (hasta 30 años) y 25 adultos (entre 30 y 60 años) y finalmente fueron 50 personas de Perú las que enviaron sus respuestas o apreciaciones. Los resultados indican que no existen diferencias significativas entre género y preferencia hacia un centro de esparcimiento natural que ofrezca equilibrio C-M-E; no obstante que, matemáticamente la mayoría prefiere un local con mobiliario natural. Tampoco existen diferencias significativas entre género y ubicación geográfica de centro de esparcimiento en “Centro poblado La Mariposa”; a la mitad le parece indiferente y a la otra mitad le parece asequible y, el citado lugar tiene las características siguientes: está alejado de la ciudad, cerca del río Piura-Perú, alejado de ruido, y rodeado de un entorno natural. Tampoco existen diferencias significativas entre género y cantidad dinero gastada en ultima enfermedad, puesto que la mitad gastó menos de S/ 1,200.00 (menos de$ 300.00) y la otra mitad gastó más de la cifra anterior. Tampoco existen diferencias significativas entre género y cantidad dinero dispuesta para 1 día en centro de esparcimiento para equilibrar C-M-E; puesto que la mitad está dispuesta a gastar menos de S/ 360.00 (menos de $120.00) y la otra mitad más de S/ 360.00 (más de$ 120.00). Tampoco existen diferencias significativas entre edad y cantidad dinero dispuesta para 1 día en centro de esparcimiento para equilibrar C-M-E, puesto que la mitad está dispuesta a gastar menos de S/ 480.00 (menos de $120.00) y la otra mitad más de S/ 480.00 (más de$ 120.00). Tampoco existen diferencias significativas entre género y disposición a solicitar los servicios de un centro de esparcimiento para equilibrar C-M-E, puesto que la mayoría definitivamente sí solicitaría dichos servicios. Las conclusiones son: (1) El programa de equilibrio C-M-E genera efectos positivos con mejor calidad de vida, alejando a las personas de la condición de vulnerabilidad ante próximas pandemias. (2) Se recomienda que, los gerentes de organizaciones privadas gestionen la construcción de centros de esparcimiento donde se aplique el programa de equilibrio C-M-E, favoreciendo a sus trabajadores y a sus clientes. (3) Del mismo modo, se recomienda que, los funcionarios de organizaciones gubernamentales soliciten los servicios de centros de esparcimiento que ofrezcan el programa de equilibrio C-M-E para colaborar con sus trabajadores y usuarios, en el logro de bienestar

Metabolomic profile of cancer stem cell-derived exosomes from patients with malignant melanoma

Malignant melanoma (MM) is the most aggressive and life-threatening form of skin cancer. It is characterized by an extraordinary metastasis capacity and chemotherapy resistance, mainly due to melanoma cancer stem cells (CSCs). To date, there are no suitable clinical diagnostic, prognostic or predictive biomarkers for this neoplasia. Therefore, there is an urgent need for new MM biomarkers that enable early diagnosis and effective disease monitoring. Exosomes represent a novel source of biomarkers since they can be easily isolated from different body fluids. In this work, a primary patient-derived MM cell line enriched in CSCs was characterized by assessing the expression of specific markers and their stem-like properties. Exosomes derived from CSCs and serums from patients with MM were characterized, and their metabolomic profile was analysed by highresolution mass spectrometry (HRMS) following an untargeted approach and applying univariate and multivariate statistical analyses. The aim of this study was to search potential biomarkers for the diagnosis of this disease. Our results showed significant metabolomic differences in exosomes derived from MM CSCs compared with those from differentiated tumour cells and also in serum-derived exosomes from patients with MM compared to those from healthy controls. Interestingly, we identified similarities between structural lipids differentially expressed in CSC-derived exosomes and those derived from patients with MM such as the glycerophosphocholine PC 16:0/0:0. To our knowledge, this is the first metabolomic-based study aimed at characterizing exosomes derived from melanoma CSCs and patients’ serum in order to identify potential biomarkers for MM diagnosis. We conclude that metabolomic characterization of CSC-derived exosomes sets an open door to the discovery of clinically useful biomarkers in this neoplasia.MICIU FPU15/03682 FPU15/02350Ministerio de Ciencia, Innovación y Universidades (MICIU) MAT2015-62644.C2.2.R RTI2018-101309-BC2Instituto de Salud Carlos III PIE16-00045Junta de Andalucía SOMM17/6109/UGR (UCE-PP2017-3)European Union (EU) SOMM17/6109/UGR (UCE-PP2017-3)Chair 'Doctors Galera-Requena in cancer stem cell research' CMC-CTS963Fundación MEDIN

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