55 research outputs found
Modelo de predicciĂłn subespacial: RegresiĂłn Multivariante Gaussiana Subespacial
[ES]El objetivo de esta tesis doctoral es contribuir al desarrollo de nuevos meÌtodos que permitan mejorar los modelos claÌsicos de regresioÌn. Los modelos de regresioÌn claÌsicos buscan la manera de hallar una variable respuesta en funcioÌn de un conjunto de variables independientes. Estas teÌcnicas persiguen un objetivo en concreto, predecir la variable dependiente. Esta situacioÌn 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 (regresioÌn lineal multivariante) de salida. La relacioÌn entre ambos conjuntos de variables tiene una uÌnica direccioÌn. 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 riÌgidos. Su adaptabilidad a diferentes situaciones es susceptible a cambios en su estructura original, lo que provoca nuevos caÌlculos computacionales que hacen maÌs complejo el desarrollo. Una de las motivaciones principales de esta tesis es la buÌsqueda 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 estadiÌsticos de diversa iÌndole, teÌcnicas de reduccioÌn dimensional y procesos gaussianos. Las teÌcnicas de reduccioÌn dimensional nos permiten, entre otras cosas, conocer mejor la estructura de nuestros datos, simplificar meÌtodos 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 teÌcnicas de reduccioÌn dimensional son en su mayoriÌa meÌtodos exploratorios que permiten describir de forma intriÌnseca datos multivariantes. Estos procedimientos reproducen sobre planos factoriales hipoteÌticos nuestros datos en funcioÌn de las variables que los representan. Aunque estas teÌcnicas estaÌn muy presentes en el anaÌlisis multivariante, su poder predictivo es bajo.
Los procesos gaussianos son modelos estadiÌsticos en los que las observaciones suceden en un dominio continuo como espacio o tiempo. El caso espacial es descrito por las teÌcnicas de krigeaje. Estos meÌtodos de interpolacioÌn basan su poder de prediccioÌn en la denominada covarianza espacial y/o temporal y la distribucioÌn normal de sus variables. La idea baÌsica de estas teÌcnicas es predecir los valores en un punto desconocido del espacio calculando un promedio ponderado de los valores cercanos conocidos.
El modelo que desarrollamos âRegresioÌn Multivariante Gaussiana Subespacialâ (MGSR) conjuga ambas corrientes estadiÌsticas. Partiendo de unas coordenadas subespaciales generadas por una teÌcnica cualquiera de reduccioÌn dimensional y asociando a eÌstas 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 muÌltiples 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
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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 platformâs 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 toolâs 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 PerĂș
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 120.00) and the other half more than S/ 360.00 (more than 120.00) and the other half more than S/ 480.00 (over 300.00) y la otra mitad gastĂł mĂĄs de la cifra anterior. Tampoco existen diferencias significativas entre gĂ©nero y cantidad dinero dispuesta para 1 dĂa en centro de esparcimiento para equilibrar C-M-E; puesto que la mitad estĂĄ dispuesta a gastar menos de S/ 360.00 (menos de 120.00). Tampoco existen diferencias significativas entre edad y cantidad dinero dispuesta para 1 dĂa en centro de esparcimiento para equilibrar C-M-E, puesto que la mitad estĂĄ dispuesta a gastar menos de S/ 480.00 (menos de 120.00). Tampoco existen diferencias significativas entre gĂ©nero y disposiciĂłn a solicitar los servicios de un centro de esparcimiento para equilibrar C-M-E, puesto que la mayorĂa definitivamente sĂ solicitarĂa 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 condiciĂłn de vulnerabilidad ante prĂłximas pandemias. (2) Se recomienda que, los gerentes de organizaciones privadas gestionen la construcciĂłn 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
Testing and Improvements of KoopaML: A Platform to Ease the Development of Machine Learning Pipelines in the Medical Domain
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
patientsâ 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, InnovaciĂłn y Universidades (MICIU)
MAT2015-62644.C2.2.R
RTI2018-101309-BC2Instituto de Salud Carlos III
PIE16-00045Junta de AndalucĂa
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-CTS963FundaciĂłn MEDIN
Clustering COVID-19 ARDS patients through the first days of ICU admission. An analysis of the CIBERESUCICOVID Cohort
Background Acute respiratory distress syndrome (ARDS) can be classified into sub-phenotypes according to different inflammatory/clinical status. Prognostic enrichment was achieved by grouping patients into hypoinflammatory or hyperinflammatory sub-phenotypes, even though the time of analysis may change the classification according to treatment response or disease evolution. We aimed to evaluate when patients can be clustered in more than 1 group, and how they may change the clustering of patients using data of baseline or day 3, and the prognosis of patients according to their evolution by changing or not the cluster.Methods Multicenter, observational prospective, and retrospective study of patients admitted due to ARDS related to COVID-19 infection in Spain. Patients were grouped according to a clustering mixed-type data algorithm (k-prototypes) using continuous and categorical readily available variables at baseline and day 3.Results Of 6205 patients, 3743 (60%) were included in the study. According to silhouette analysis, patients were grouped in two clusters. At baseline, 1402 (37%) patients were included in cluster 1 and 2341(63%) in cluster 2. On day 3, 1557(42%) patients were included in cluster 1 and 2086 (57%) in cluster 2. The patients included in cluster 2 were older and more frequently hypertensive and had a higher prevalence of shock, organ dysfunction, inflammatory biomarkers, and worst respiratory indexes at both time points. The 90-day mortality was higher in cluster 2 at both clustering processes (43.8% [n = 1025] versus 27.3% [n = 383] at baseline, and 49% [n = 1023] versus 20.6% [n = 321] on day 3). Four hundred and fifty-eight (33%) patients clustered in the first group were clustered in the second group on day 3. In contrast, 638 (27%) patients clustered in the second group were clustered in the first group on day 3.Conclusions During the first days, patients can be clustered into two groups and the process of clustering patients may change as they continue to evolve. This means that despite a vast majority of patients remaining in the same cluster, a minority reaching 33% of patients analyzed may be re-categorized into different clusters based on their progress. Such changes can significantly impact their prognosis
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