Systematic Approaches for Telemedicine and Data Coordination for COVID-19 in Baja California, Mexico

Conference proceedings info: ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologies Raleigh, HI, United States, March 24-26, 2023 Pages 529-542We provide a model for systematic implementation of telemedicine within a large evaluation center for COVID-19 in the area of Baja California, Mexico. Our model is based on human-centric design factors and cross disciplinary collaborations for scalable data-driven enablement of smartphone, cellular, and video Teleconsul-tation technologies to link hospitals, clinics, and emergency medical services for point-of-care assessments of COVID testing, and for subsequent treatment and quar-antine decisions. A multidisciplinary team was rapidly created, in cooperation with different institutions, including: the Autonomous University of Baja California, the Ministry of Health, the Command, Communication and Computer Control Center of the Ministry of the State of Baja California (C4), Colleges of Medicine, and the College of Psychologists. Our objective is to provide information to the public and to evaluate COVID-19 in real time and to track, regional, municipal, and state-wide data in real time that informs supply chains and resource allocation with the anticipation of a surge in COVID-19 cases. RESUMEN Proporcionamos un modelo para la implementación sistemática de la telemedicina dentro de un gran centro de evaluación de COVID-19 en el área de Baja California, México. Nuestro modelo se basa en factores de diseño centrados en el ser humano y colaboraciones interdisciplinarias para la habilitación escalable basada en datos de tecnologías de teleconsulta de teléfonos inteligentes, celulares y video para vincular hospitales, clínicas y servicios médicos de emergencia para evaluaciones de COVID en el punto de atención. pruebas, y para el tratamiento posterior y decisiones de cuarentena. Rápidamente se creó un equipo multidisciplinario, en cooperación con diferentes instituciones, entre ellas: la Universidad Autónoma de Baja California, la Secretaría de Salud, el Centro de Comando, Comunicaciones y Control Informático. de la Secretaría del Estado de Baja California (C4), Facultades de Medicina y Colegio de Psicólogos. Nuestro objetivo es proporcionar información al público y evaluar COVID-19 en tiempo real y rastrear datos regionales, municipales y estatales en tiempo real que informan las cadenas de suministro y la asignación de recursos con la anticipación de un aumento de COVID-19. 19 casos.ICICT 2023: 2023 The 6th International Conference on Information and Computer Technologieshttps://doi.org/10.1007/978-981-99-3236-

Marco de referência para a gestão de programas em e-saúde

Introduction: This article derives from the research “Innovation, complexity, and management in socio-technical systems”. It was conducted between 2014 and 2017 at the Center for Scientific Research and Higher Education of Ensenada, Baja California, Mexico. The systems for processing, distributing, and transporting digital information currently have a great impact on the operation of the healthcare industry, creating opportunities to improve its services and coverage and seeking to streamline health information processes of an administrative, clinical and operational nature. A perspective of electronic health or digital health (e-health) from the science of complexity can extract new findings towards the understanding of the e-health ecosystem at the local, regional and national levels, increasing its effectiveness as a potential enabler of social welfare. Methodology: This article turns to the science of complexity as a methodological basis to develop a frame of reference, considering the interactions between agents of the ecosystem and the characteristics of the context where e-health programs and interventions will be implemented. Results: A frame of reference is presented to address e-health programs, projects and interventions from a comprehensive perspective taking into account the role played by the interactions between predominant agents of the system and the characteristics of the context that will be intervened. As an example of application of the proposed frame of reference, the project “Tele-epidemiology of vector-borne diseases” being carried out by our research group is briefly described. Conclusions: The proposed frame of reference provides a platform for analysis and management that seeks to take advantage of the potential of e-health to become an effective vehicle for solidarity and social development.Introducción: producto de la investigación “Innovación, complejidad y gestión en sistemas sociotécnicos”, desarrollada entre 2014-2017 en el Centro de Investigación Científica y Educación Superior de Ensenada, Baja California, México. Los sistemas de procesamiento, distribución y transporte de información digital tienen hoy gran impacto en la operación del sector salud, creando oportunidades para mejorar sus servicios, cobertura y los procesos de información sanitaria de carácter administrativo, clínico y operativo. Una perspectiva de la salud electrónica o salud digital (e-salud) desde la ciencia de la complejidad puede extraer nuevos hallazgos hacia el entendimiento de su ecosistema en los ámbitos local, regional y nacional, lo que aumenta su efectividad como potencial habilitador de bienestar social. Metodología: se utilizó la ciencia de la complejidad para desarrollar un marco de referencia, teniendo en cuenta las interacciones entre los agentes del ecosistema y las características del contexto donde los programas e intervenciones de e-salud serán implantados. Resultados: se presenta un marco de referencia para abordar programas, proyectos e intervenciones de e-salud desde una perspectiva integral; se tiene en cuenta el papel que juegan las interacciones de los agentes preponderantes del sistema y las características del contexto que se intervendrá. Como ejemplo de aplicación del marco de referencia propuesto, se describe el planteamiento de proyecto de “Teleepidemiología de enfermedades transmitidas por vector” en proceso de desarrollo por nuestro grupo de investigación. Conclusiones: el marco de referencia propuesto provee una plataforma de análisis y gestión que intenta aprovechar el potencial de la e-salud para convertirse en vehículo efectivo de solidaridad y desarrollo social.Introdução: o artigo é produto da pesquisa “Inovação, complexidade e gestão em sistemas sociotécnicos”, que foi desenvolvida entre 2014 e 2017 no Centro de Pesquisa Científica e Educação Superior de Ensenada, Baixa Califórnia, no México. Os sistemas de processamento, distribuição e transporte de informação digital têm atualmente grande impacto na operação do setor de saúde, o que cria oportunidades para melhorar seus serviços e cobertura e para tentar tornar eficientes os processos de informação sanitária de caráter administrativo, clínico e operativo. Uma perspectiva da saúde eletrônica ou saúde digital (e-saúde) desde a ciência da complexidade pode extrair novas descobertas para o entendimento do ecossistema da e-saúde nos âmbitos local, regional e nacional, o que aumenta sua efetividade como potencial habilitador de bem-estar social. Metodologia: neste artigo recorremos à ciência da complexidade como base metodológica para desenvolver um marco de referência levando em consideração as interações entre os agentes do ecossistema e as características do contexto no qual os programas e intervenções de e-saúde serão implantados. Resultados: apresenta-se um marco de referência para abordar os programas, projetos e intervenções de e-saúde desde uma perspectiva integral, levando em consideração o papel das interações dos agentes preponderantes do sistema e as características do contexto que passará por intervenção. Como exemplo de aplicação do marco de referência proposto, descreve-se brevemente a concepção do projeto de “Tele-epidemiologia de doenças transmitidas por vetor” em processo de desenvolvimento por nosso grupo de pesquisa. Conclusões: o marco de referência proposto fornece uma plataforma de análise e gestão que tenta aproveitar o potencial da e-saúde para se converter em um veículo efetivo de solidariedade e desenvolvimento social

ACM DIGITAL LIBRARY

Mental health from the pandemic generated in 2019 with SARSCOV19, has increased, likewise contributed to relapse and exacerbation of mental health symptoms in diagnosed patients. In addition, individuals with a recent diagnosis of a mental disorder were found to have a higher risk of COVID-19 infection and also a higher frequency of adverse outcomes, representing an additional risk factor for worsening mental health. The aim of this research is to develop a technological solution is the development and implementation of a web platform and a mobile application to assist and support the therapeutic work related to schizophrenia, both in its aspect of continuous assessment, as well as intervention in different areas. A mobile application is developed to be used to send the tests to the patients in order to evaluate their mental state and, in this way, to foresee possible relapses. A web application was developed for doctors to administer users and ask questions together with the consultation of test results. As result, an average of 87.11 was obtained in the SUS test of the application and 78.54 in the web test. This test evaluates usability and a score higher than 68 is considered good.Conference UBICOMP UbiComp: Ubiquitous Computinghttps://dl.acm.org/conference/ubicom

UbiComp/ISWC ’23 Adjunct Adjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing

Páginas 655–657Mental health from the pandemic generated in 2019 with SARSCOV19, has increased, likewise contributed to relapse and exacerbation of mental health symptoms in diagnosed patients. In addition, individuals with a recent diagnosis of a mental disorder were found to have a higher risk of COVID-19 infection and also a higher frequency of adverse outcomes, representing an additional risk factor for worsening mental health. The aim of this research is to develop a technological solution is the development and implementation of a web platform and a mobile application to assist and support the therapeutic work related to schizophrenia, both in its aspect of continuous assessment, as well as intervention in different areas. A mobile application is developed to be used to send the tests to the patients in order to evaluate their mental state and, in this way, to foresee possible relapses. A web application was developed for doctors to administer users and ask questions together with the consultation of test results. As result, an average of 87.11 was obtained in the SUS test of the application and 78.54 in the web test. This test evaluates usability and a score higher than 68 is considered good. RESUMEN La salud mental a partir de la pandemia generada en 2019 con el SARSCOV19, ha aumentado, de igual manera contribuyó a la recaída y exacerbación de síntomas de salud mental en pacientes diagnosticados. Además, se descubrió que las personas con un diagnóstico reciente de un trastorno mental tenían un mayor riesgo de infección por COVID-19 y también una mayor frecuencia de resultados adversos, lo que representa un factor de riesgo adicional para el empeoramiento de la salud mental. El objetivo de esta investigación es desarrollar una solución tecnológica es el desarrollo e implementación de una plataforma web y una aplicación móvil para ayudar y apoyar el trabajo terapéutico relacionado con la esquizofrenia, tanto en su vertiente de evaluación continua, como de intervención en diferentes áreas. . Desarrollan una aplicación móvil para enviar los test a los pacientes para evaluar su estado mental y, de esta forma, prever posibles recaídas. Se desarrolló una aplicación web para que los médicos administren usuarios y realicen preguntas junto con la consulta de los resultados de las pruebas. Como resultado, un promedio se obtuvo una puntuación de 87,11 en la prueba SUS de la aplicación y de 78,54 en la prueba web. Esta prueba evalúa la usabilidad y una puntuación superior a 68 se considera buena.https://doi.org/10.1145/3594739.361288

Apliedd sciencies

Convolutional neural networks and deep learning models represent the gold standard in medical image classification. Their innovative architectures have led to notable breakthroughs in image classification and feature extraction performance. However, these advancements often remain underutilized in the medical imaging field due to the scarcity of sufficient labeled data which are needed to leverage these new features fully. While many methodologies exhibit stellar performance on benchmark data sets like DDSM or Minimias, their efficacy drastically decreases when applied to real-world data sets. This study aims to develop a tool to streamline mammogram classification that maintains high reliability across different data sources. We use images from the DDSM data set and a proprietary data set, YERAL, which comprises 943 mammograms from Mexican patients. We evaluate the performance of ensemble learning algorithms combined with prevalent deep learning models such as Alexnet, VGG-16, and Inception. The computational results demonstrate the effectiveness of the proposed methodology, with models achieving 82% accuracy without overtaxing our hardware capabilities, and they also highlight the efficiency of ensemble algorithms in enhancing accuracy across all test cases.MDPI Academic Open Access Publishinghttps://www.mdpi.com/journal/applsc

Ensemble Deep Learning Models for Heart Disease Classification: A Case Study from Mexico

Heart diseases are highly ranked among the leading causes of mortality in the world. They have various types including vascular, ischemic, and hypertensive heart disease. A large number of medical features are reported for patients in the Electronic Health Records (EHR) that allow physicians to diagnose and monitor heart disease. We collected a dataset from Medica Norte Hospital in Mexico that includes 800 records and 141 indicators such as age, weight, glucose, blood pressure rate, and clinical symptoms. Distribution of the collected records is very unbalanced on the different types of heart disease, where 17% of records have hypertensive heart disease, 16% of records have ischemic heart disease, 7% of records have mixed heart disease, and 8% of records have valvular heart disease. Herein, we propose an ensemble-learning framework of different neural network models, and a method of aggregating random under-sampling. To improve the performance of the classification algorithms, we implement a data preprocessing step with features selection. Experiments were conducted with unidirectional and bidirectional neural network models and results showed that an ensemble classifier with a BiLSTM or BiGRU model with a CNN model had the best classification performance with accuracy and F1-score between 91% and 96% for the different types of heart disease. These results are competitive and promising for heart disease dataset. We showed that ensemble-learning framework based on deep models could overcome the problem of classifying an unbalanced heart disease dataset. Our proposed framework can lead to highly accurate models that are adapted for clinical real data and diagnosis use

Effect of intramuscular baculovirus encoding mutant hypoxia-inducible factor 1-alpha on neovasculogenesis and ischemic muscle protection in rabbits with peripheral arterial disease

Background aims: Peripheral arterial disease (PAD) is a progressive, disabling ailment for which no effective treatment exists. Gene therapy-mediated neovascularization has emerged as a potentially useful strategy. We tested the angiogenic and arteriogenic efficacy and safety of a baculovirus (BV) encoding mutant, oxygen-resistant hypoxia-inducible factor 1-alpha (mHIF-1α), in rabbits with PAD. Methods: After assessing the transfection efficiency of the BV.mHIF-1α vector and its tubulogenesis potential in vitro, we randomized rabbits with experimental PAD to receive 1 × 109 copies of BV.mHIF-1α or BV.null (n = 6 per group) 7 days after surgery. Two weeks post-treatment, collateralization (digital angiography) and capillary and arteriolar densities (immunohistochemistry) were measured in the posterior limbs. Ischemic damage was evaluated in adductor and gastrocnemius muscle samples. Tracking of viral DNA in injected zones and remote tissues at different time points was performed in additional rabbits using a BV encoding GFP. Results: Angiographically visible collaterals were more numerous in BV.mHIF-1α-treated rabbits (8.12 ± 0.42 vs 6.13 ± 1.15 collaterals/cm2, P < 0.05). The same occurred with arteriolar (27.9 ± 7.0 vs 15.3 ± 4.0 arterioles/mm2) and capillary (341.8 ± 109.9 vs 208.8 ± 87.7 capillaries/mm2, P < 0.05) densities. BV.mHIF-1α-treated rabbits displayed less ischemic muscle damage than BV.null-treated animals. Viral DNA and GFP mRNA were detectable only at 3 and 7 days after injection in hind limbs. Neither the virus nor GFP mRNA was detected in remote tissues. Conclusions: In rabbits with PAD, BV.mHIF-1α induced neovascularization and reduced ischemic damage, exhibiting a good safety profile at 14 days post-treatment. Complementary studies to evaluate its potential usefulness in the clinic are needed.Fil: Giménez, Carlos Sebastián. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Castillo Velasquez, Martha Giovanna. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Simonin, Jorge Alejandro. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Ingeniería Genética y Biología Molecular y Celular; ArgentinaFil: Nuñez Pedrozo, Cristian Nahuel. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Pascuali, Natalia Marisa. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental. Fundación de Instituto de Biología y Medicina Experimental. Instituto de Biología y Medicina Experimental; ArgentinaFil: Bauza, Maria del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Locatelli, Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: López, Ayelén Emilce. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Belaich, Mariano Nicolas. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Quilmes. Departamento de Ciencia y Tecnología. Laboratorio de Ingeniería Genética y Biología Molecular y Celular; ArgentinaFil: Mendiz, Alfredo O.. Universidad Favaloro; ArgentinaFil: Crottogini, Alberto José. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Cuniberti, Luis Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; ArgentinaFil: Olea, Fernanda Daniela. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Houssay. Instituto de Medicina Traslacional, Trasplante y Bioingeniería. Fundación Favaloro. Instituto de Medicina Traslacional, Trasplante y Bioingeniería; Argentin