Ciencias Exactas y Ciencias de la Salud
Permanent URI for this collectionhttps://hdl.handle.net/11285/551039
Pertenecen a esta colección Tesis y Trabajos de grado de las Maestrías correspondientes a las Escuelas de Ingeniería y Ciencias así como a Medicina y Ciencias de la Salud.
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- COVID-19 mortality prediction using deep neural networks(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2022-06) García Zendejas, Arturo; MORALES MENENDEZ, RUBEN; 30452; REPOSITORIO NACIONAL CONACYT; Morales Menéndez, Rubén; emipsanchez; School of Engineering and Sciences; Campus MonterreyCOVID - 19 disease caused by the virus SARS-CoV2 appeared in Wuhan China in 2019, in March 11th 2020 it was declared a global pandemics, taking by March 2022 over 5,783,700 lives around the world. COVID-19 spreads in several different ways, the virus SARS-CoV2 which causes COVID-19 can spread from a mouth or nose of a person who is infected through liquid particles whenever they cough, sneeze, speak or breath. Initial symptoms and development of the illness are catalogued as mild, because of that it may be difficult to identify which persons will more probably develop severe disease. One great support that can be given to medical centers and healthcare workforce would be the ability to predict which patients will have a greater risk of death and would develop more quickly and severe illness, in order to make triage for treatment and decisions about resources distribution. Machine learning and specifically Deep Learning works by modelling hierarchical representations behind data, aiming to classify or predict patterns by stacking multiple layers of information. Some of its main applications are speech recognition, natural language processing, audio recognition, autonomous vehicles and even medicine. In medicine, it has been used to predict how a disease develops and affects patients. During this thesis it was done a research and comparison of state of the art articles and models that aim to predict the behavior and development of COVID-19 patients and the illness itself. Their different datasets, metrics, models and results have been studied and used as a base in order to create the proposed models of the thesis. This research project proposes the use of machine learning models to predict the mortality of COVID-19 patients by using as input attributes of the patients such as vital signs, biomarkers, comorbidities and diagnostics. This data was obtained for training and testing purposes from different medical centers, such as HM Hospitals, San Jose Hospital and CEM Hospital. The main Deep Learning model used during this thesis is a Deep Multi-layer Perceptron Neural Network which uses static attributes, and a Long-Short Term Memory Recurrent Neural Network using dynamic attributes. A mixed model combining the static and dynamic model was also created. It was also used metrics that support the reduction of false negative cases, the Maximum Probability of Correct Decision is the main metric to evaluate and optimize the model. The models have been evaluated and compared with another machine learning models such as Random Forest and eXtreme Gradient Boosting over the different datasets.
- Immunomodulatory effect of a nutraceutical mixture in a mouse model of metabolic syndrome(Instituto Tecnológico y de Estudios Superiores de Monterrey, 2021-05-28) Gastélum Estrada, Alejandro; Serna Saldívar, Sergio Román Othón; puelquio; Santacruz López, Yolanda Arlette; School of Engineering and Sciences; Campus Monterrey; Canales Aguirre, Alejandro ArturoCOVID-19 has impacted global community since its appearance in December 2019, with consequences in health, economic, employment, among many others that have created scenarios known as “new normality”. Along pharmacological measures, preventive ones have also been proposed including the change of diet patterns, increasing physical activities and others. In this work, nutraceuticals are explored for assessing their potential as COVID-19 preventers that could extrapolate to other new diseases or pandemics. Specifically, a nutraceutical mixture was tested in C57BL/6J mice, which is a model for obesity and metabolic syndrome, to evaluate immunomodulation potential by measuring the effect on blood indicators and immune biomarkers. Nutraceuticals evaluated include vitamins (C, D and E), minerals (selenium and zinc) and other ingredients as coenzyme Q10, microencapsulated probiotics, broccoli sprout powder and black bean coat flour as sources of sulforaphane and flavonoids, respectively. All of them have been widely studied and attributed with immunomodulatory properties, each one of them are explained and detailed in the second chapter. Results of blood indicators show a low effect on blood cells concentration and lipid profile, with no consistent differences between male and female individuals. No significant effect was determined in coagulation time. Some of the observed changes such as increase of erythrocytes and leukocyte in males of the supplemented group may suggest a heterogeneous effect between male and female mice, but more studies would be needed. While no significant effects were observed in lymphocyte-T analysis, the most relevant result was obtained in IL-1 evaluation, which level significantly increased in the obese-no supplemented group in comparison with the healthy group, but the increase was countered and even got to lower levels compared to healthy mice when the nutraceutical supplement was included in the diet. This result may suggest a higher effect of the nutraceuticals in inflammation processes rather than in blood cell levels.