Modelo para análisis de riesgo de la diabetes mellitus 2 usando inteligencia de negocios y minería de datos

La diabetes mellitus tipo 2 (DM2) es una enfermedad crónica caracterizada por una hiperglucemia y trastornos en el metabolismo de las grasas, hidratos de carbono y proteínas de forma tal que genera defectos en la producción y acción de la insulina en el cuerpo. Esta enfermedad presenta complicaciones crónicas que deterioran la calidad de vida de los pacientes y aumentan significativamente el riesgo de muerte. Para Colombia, es claro que se debe tener una prioridad en la detección temprana y aseguramiento de intervenciones para la diabetes mellitus 2. Este documento de tesis presenta un modelo de análisis de riesgo de la DM2 basado en inteligencia de negocios y minería de datos, el cual permite integrar y transformar datos clínicos, caracterizar pacientes y describirlos teniendo en cuenta sus diagnósticos, consumos y entorno social. Adicionalmente, el modelo permite predecir si un paciente puede o no sufrir comorbilidad asociada a DM2 y de qué tipo puede ser. La caracterización de pacientes se realiza a través de un algoritmo de agrupación y la descripción se hace mediante el uso de reglas de asociación. El modelo de predicción por su parte, utiliza _arboles de decisión y redes bayesianas. El caso de estudio consistió de una cohorte de 14162 pacientes reales enfermos de DM2 proporcionados por la empresa Processum LTDA, con registros de diagnósticos, procedimientos clínicos y variables socio culturales desde el año 2009 hasta el 2012.Abstract. Type 2 Diabetes mellitus (T2DM) is a chronic disease characterized by hyperglycemia and disorders in the metabolism of fat, carbohydrate and protein in a manner that produces defects in the production and action of insulin in the body. This disease presents chronic complications that deteriorate patients life quality and significantly increase the risk of death. In Colombia, it is clear that ensuring early detection and intervention for type 2 diabetes mellitus should be a priority. This thesis paper presents a model for risk analysis of DM2 based on business intelligence and data mining. This model allows to characterize and describe patients by using their diagnoses, clinical procedures and social environment records. Additionally, the model can predict whether a patient may or may not sufier T2DM related comorbidity and which type of comorbidity it can be. Patients characterization is performed through a clustering algorithm, and the description is made by using the association rules. The prediction model uses decision trees and Bayesian networks. The case study consisted of a cohort of 14162 patients with DM2 real patients provided by the company processum LTDA, with records of diagnoses, clinical procedures and sociocultural variables from 2009 to 2012.Maestrí

Explaining mortality rates from COVID-19 : an application of business analytics

The COVID-19 pandemic has generated a lot of demand for responses to prevention, treatment, how to control, how to predict evolutions, among others. This thesis aims to answer the question about what affects mortality. Thus, through the use of Analytics, 26 different variables were studied for 37 duly selected countries. The results showed that the country's economic structure has no impact on mortality, while vaccination policy for BCG, changes in mobility within the country, such as “stay at home”, and the prevalence of diabetes have an impact on mortality.A pandemia COVID-19 tem gerado muita procura por respostas para prevenção, tratamento, como controlar, como prever evoluções, entre outras. Esta tese pretende responder à pergunta sobre o que afeta a mortalidade. Assim, através do uso do Analytics foram estudadas 26 diferentes variáveis para 37 países devidamente selecionados. Os resultados permitiram concluir que a estrutura económica do país não tem impacto na mortalidade, enquanto que a política de vacinação para a BCG, as alterações da mobilidade dentro do país, tais como o “stay at home”, e a prevalência de diabetes têm impacto para a mortalidade

Data Science in Healthcare

Data science is an interdisciplinary field that applies numerous techniques, such as machine learning, neural networks, and deep learning, to create value based on extracting knowledge and insights from available data. Advances in data science have a significant impact on healthcare. While advances in the sharing of medical information result in better and earlier diagnoses as well as more patient-tailored treatments, information management is also affected by trends such as increased patient centricity (with shared decision making), self-care (e.g., using wearables), and integrated care delivery. The delivery of health services is being revolutionized through the sharing and integration of health data across organizational boundaries. Via data science, researchers can deliver new approaches to merge, analyze, and process complex data and gain more actionable insights, understanding, and knowledge at the individual and population levels. This Special Issue focuses on how data science is used in healthcare (e.g., through predictive modeling) and on related topics, such as data sharing and data management

Bibliometric Studies and Worldwide Research Trends on Global Health

Global health, conceived as a discipline, aims to train, research and respond to problems of a transboundary nature, in order to improve health and health equity at the global level. The current worldwide situation is ruled by globalization, and therefore the concept of global health involves not only health-related issues, but also those related to the environment and climate change. Therefore, in this Special Issue, the problems related to global health have been addressed from a bibliometric approach in four main areas: environmental issues, diseases, health, education and society

Assessment and risk stratification of ageing-related target organ damage and adverse health outcomes in the general population

The objectives of this doctoral thesis are to address the contribution of blood pressure to the presence of subclinical target-organ damage and the development of adverse health complications that associate with a poor life course of aging. This thesis focuses on ambulatory blood pressure monitoring to provide the most accurate information about the blood pressure level and variability over a 24-hour period. Moreover, by investigating the role of novel markers, including imaging markers and biomarkers, this thesis also provides possible pathophysiological and biological mechanisms that might explain the association between vascular risk factors and adverse health complications. We envisage that the results of our study will contribute to the refinement of risk stratification of major micro- (ophthalmological, neurological) and macro‑vascular (neurological, cardiovascular) complications associated with poor aging