Rodríguez Aguilar, Román
Main Affiliation
Preferred name
Rodríguez Aguilar, Román
Official Name
Rodríguez Aguilar, Román
ORCID
0000-0002-6496-4453
Researcher ID
AAO-6158-2020
Scopus Author ID
56717663000
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Item type:Publication, Modeling the Relation Between Non-Communicable Diseases and the Health Habits of the Mexican Working Population: A Hybrid Modeling Approach(MDPI AG, 2025); The impact that Non-Communicable Diseases (NCDs) have on the health status of the population has generated the need for an in-depth analysis of health habits and NCDs. In addition to its significant impact on population health, this phenomenon also translates into substantial economic consequences for countries. This study delves into the analysis of the relationship between health habits and NCDs among the economically active population of Mexico. Through a hybrid approach that integrates the use of machine learning (ML) models and a structural equation model (SEM), we seek to quantify the direct and indirect causal effects between health habits and NCDs. For this study, information from the 2022 National Health and Nutrition Survey carried out in Mexico for the working-age population is used. According to the results obtained in the first stage of analysis using ML, the most relevant variables (health habits) that impact the probability of individuals presenting with NCDs were identified (random forest precision of 78.66% and Lasso with 71.27%). The second stage of analysis through SEM using the most relevant variables, which were selected through ML, allowed us to measure the direct and indirect causal effect of health habits on NCDs. The SEM model was statistically significant (Chi-square: 449.186; p-value = 0.0000) and revealed that negative health habits, such as a poor diet, physical inactivity, smoking and alcohol consumption, significantly increase the risk of NCDs in the working-age population in Mexico (0.23), while vigorous physical activity and salary has a negative impact (−0.17 and −0.23, respectively) on the presence of NCDs. This study highlights the ability of machine learning and SEM approaches to model the impact of health habits on NCDs for the economically active population in Mexico. ©The authors © Mathematics ©MDPI. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Digital Transformation and XAI in HealthcareThis book explores the pivotal role of explainable artificial intelligence (XAI) in driving digital transformation within the healthcare sector, providing comprehensive insights into its applications, ethical and legal considerations, technological requirements, and future trends. Digital Transformation and XAI in Healthcare delves into the fundamental role of XAI in transforming healthcare, addressing critical issues such as data security, ethical considerations, and the integration of XAI into existing healthcare infrastructures. By offering a comprehensive overview of technological tools, infrastructure requirements, and legal frameworks, this book equips healthcare professionals with the knowledge to navigate the complexities of XAI applications. The book explores the future of healthcare education and the pivotal role of XAI in training the next generation of healthcare professionals. It discusses how XAI can enhance learning experiences and provide more personalized education, ensuring that future clinicians are well equipped to utilize advanced AI technologies. It also delves into the technological tools and infrastructure required for implementing XAI, as well as data management and privacy concerns. The exploration of global collaborations and innovative projects highlights the book's unique perspective on the international impact of XAI in healthcare. Intended for healthcare professionals, researchers, and students, this book will provide valuable insights into the future of healthcare technology. Readers will be equipped with the knowledge to harness the power of XAI, ensuring that AI systems are not only accurate but also transparent, trustworthy, and ethically sound. ©The authors ©CRC Press. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks ApplicationArtificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. ©The authors ©MDPI. - Some of the metrics are blocked by yourconsent settings
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Item type:Publication, Financial risk of increasing the follow-up period of breast cancer treatment currently covered by the Social Protection System in Health in México(2018); Scopus© Citations 10 13 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Backbone Distribution Network Design for the Mexican Automotive Industry(2020); Pedraza-Arroyo, ErikaThe logistics network of an automotive company in Mexico was analyzed to propose a better logistics network in the country to improve delivery times to customers. The analysis was conducted with Greenfield Analysis and Network Optimization. Taking into account the information given by the company, it was possible to obtain optimal scenarios for better operation, which involved the construction of distribution centers in the Hidalgo state in Mexico. © Springer Nature Switzerland AG 2020.23 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Proposal for a comprehensive environmental key performance index of the green supply chain(2020)The consideration of environmental objectives in the design of green supply chains creates the need to build a set of key performance indicators for monitoring and control. There is a set of generally accepted environmental indicators for monitoring environmental objectives in the supply chain. However, so far these indicators have been disconnected from the operational and economic indicators of the supply chain. It is important to consider these elements in the integral performance of a green supply chain. The present work is a proposal of a general index of environmental performance that includes operational, financial, and environmental aspects that allow monitoring the integral performance of the supply chain by applying Principal Component Analysis of mixed data. © 2020, Springer Science+Business Media, LLC, part of Springer Nature.Scopus© Citations 4 11 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Health 4.0, Prevention, and Health Promotion in Companies: A Systematic Literature ReviewNoncommunicable diseases are growing worldwide and their impact within organizations affects the productivity of companies. The accelerated pace of life, sedentary lifestyle, eating habits, and lack of self-regulation have deteriorated workers’ health conditions. The Health 4.0 paradigm can help in health prevention and promotion thanks to the use of smart devices and digital tools adaptable to users and companies. Trials from 11 bibliographic databases were consulted and out of a total of 742 articles, 86 were selected that met the selection criteria. There is scientific evidence that supports the use of smart devices in companies focusing on weight control, physical activity, sleep control, and glycemic index to impact the treatment and prevention of noncommunicable diseases such as diabetes, overweight, work stress, cardiovascular diseases, and in lifestyle. Using wearables or smartphones, incentive programs or assistance with specialists have been considered by some researchers; elements such as privacy and information security are essential in the implementation, as well as methods that can maintain the use of these prevention and health promotion programs. More research is necessary regarding the use of smart devices such as the permanence of health initiatives in companies, cost-effectiveness, and real-time analysis, and focus on various pathological conditions for success in prevention and health promotion strategies.Scopus© Citations 1 40 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Modeling the Optimal Supply Chain of Liquefied Natural Gas as Fuel in Fishing Vessels in Mexico(2022); Venegas-Martínez, FranciscoThe global energy transition process has generated a set of modifications in the generation and consumption of energy. Environmental objectives have gained great relevance for regions, countries and companies. The fishing sector has been identified as having a broad environmental impact, which is why the transition to cleaner energy sources in this sector has been considered. One of the proposed strategies is based on the transition from the diesel engines of the ships to the use of liquefied natural gas (LNG), however, this transition requires guaranteeing the supply of fuel as well as the process of reconversion of units in operation and the impulse of LNG gas engines for new units. This work presents a proposal for the design of an LNG gas supply chain for the fishing industry in the State of Tampico in Mexico that allows evaluating the feasibility of the transition from the use of diesel to natural gas in fishing vessels. The main results show the feasibility of the transition in the fuel supply and economic and environmental benefits for the fishing industry. However, there is a significant challenge in converting units in operation to the use of natural gas due to the lack of public policies that promote and support its use in this sector. © Mobile Networks and Applications14 2 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Preface: Intelligent Computing and Optimization : Proceedings of the 6th International Conference on Intelligent Computing and Optimization 2023 (ICO2023) : Volume 2(Springer, 2023); Panchenko, VladimirThe sixth edition of the International Conference on Intelligent Computing and Optimization (ICO’2023) was held during April 27–28, 2023, at G Hua Hin Resort and Mall, Hua Hin, Thailand. The objective of the international conference is to bring the global research scholars, experts and scientists in the research areas of intelligent computing and optimization from all over theworld to share their knowledge and experiences on the current research achievements in these fields. This conference provides a golden opportunity for global research community to interact and share their novel research results, findings and innovative discoveries among their colleagues and friends. The proceedings of ICO’2023 is published by SPRINGER (in the book series Lecture Notes in Networks and Systems) and indexed by SCOPUS. ©2023 Springer, ©2023 The authors.27