Martínez Velasco, Antonieta Teodora
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Preferred name
Martínez Velasco, Antonieta Teodora
Official Name
Martínez Velasco, Antonieta Teodora
Alternative Name
amartinezv
Main Affiliation
ORCID
Scopus Author ID
57192978286
Researcher ID
DWK-4326-2022

Research Output
Now showing 1 - 10 of 25
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Addressing Class Imbalance in Healthcare Data: Machine Learning Solutions for Age-Related Macular Degeneration and Preeclampsia

2024 , Martínez Velasco, Antonieta Teodora , Martinez-Villaseñor, Lourdes , Luis Miralles-Pechuán

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Balancing Work, Family, and Personal Life in the Mexican Context: The Future of Work for the “COVID-19 Generation”

2021 , Scalzo, Germán , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora

Intergenerational talent management—i.e., attracting and retaining employees across generations and with different motivations—is one of companies’ greatest challenges. The expectations that recent generations bring with them have pushed culture in the direction of work-family balance, which is now seen as a key tool for human resources departments in charge of creating support mechanisms to attract and retain the next generation of workers. This trend has been reinforced by the changes brought about in light of the COVID-19 pandemic. Responding to this shift, and inspired by the challenges that our “new normal” posits, this chapter presents research results from a survey conducted in Mexico with respondents from generations Y and Z. The survey results offer important insight into how these generations perceive work-life balance, as well as the expectations that young Mexicans between the ages of 18 and 30 hold in terms of family and work.

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What Drives Profit Income in Mexico’s Main Banks? Evidence Using Machine Learning

2023 , González-Rossano, Carlos , Terán-Bustamante, Antonia , Velázquez-Salazar, Marisol , Martínez Velasco, Antonieta Teodora

Historically, the banking system has been critical to the development of economies by addressing funds efficiently—from customer savings and investors to the productive activities of people and companies, financing consumer goods and current expenses, housing, infrastructure projects and providing liquidity to the market. However, it must be transformed to respond to emerging demands in society for better financial products and services with a positive impact on living conditions and well-being. To achieve this, banks must create economic value—that is to say, banks should create profits in a sustained manner—in order to also create social value and thus generate shared value. The purpose of this study was twofold. The first aim was to identify the main factors that contributed to the majority of Mexican banking profits in the period from 2003 to 2021; the second aim of the study was to provide an innovative metric of banking performance. Using supervised machine learning algorithms and Principal Component Analysis, two prediction models were tested, and two banking performance indices were defined. The findings show that Random Forest is a reliable profit prediction model with a lower mean absolute error between the predicted yearly profit and losses and the actual data. There are no significant ranking position differences between the two performance indices. The first performance index obtained is novel due to its simplicity, since it is built on the basis of five values associated with commercial banking activity. In Mexico, no similar studies have been published. The indicator most widely used by regulators worldwide is the CAMELS index, which is a weighted average of the capital adequacy level, asset quality, management capacity, profitability, liquidity, and sensitivity to market risk. Its scale of 1 to 5 is useful for identifying the robustness and solvency of a bank, but not necessarily its capacity to generate profits. This approach might encourage banks to remain aware of their potential to create shared value and to develop competitive strategies to increase benefits for stakeholders. © 2023 by the authors.

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Confiabilidad y validez de un instrumento de selección de capital humano

2020 , Terán-Bustamante, Antonia , Castillo Ramírez, Claudia Estrella , Martínez Velasco, Antonieta Teodora

El objetivo de esta investigación es analizar la validación y confiabilidad de un instrumento para la selección de capital humano en las organizaciones enfocado a la confianza-integridad. Para evaluar la confiabilidad y validez del instrumento se realizan dos tipos de análisis, el primero con modelos de la teoría clásica mediante el Alpha de Cronbach y el estadístico Kuder-Richardson 20. El segundo análisis se realiza utilizando la Teoría de Respuesta al Item (TRI) a través del modelo de Rasch, utilizando programación en R. El test desarrollado se realiza con enfoque en psicopatía organizacional. La evidencia encontrada muestra confiabilidad y validez en la prueba analizada, tanto para la valoración de las preguntas como para la fiabilidad en la selección de los individuos analizados. Así mismo, el modelo de Rach aporta información valiosa a nivel preguntas y sujetos para tener un mejor instrumento. Esta investigación aporta conocimiento en dos sentidos i) en los test de personalidad aplicados en las organizaciones para la selección de capital humano en forma más confiable y ii) en mostrar una metodología

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University–Industry Collaboration: A Sustainable Technology Transfer Model

2021 , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora , López-Fernández, Andreé Marie

Faced with the pandemic caused by COVID-19, universities worldwide are giving a powerful response to support their communities. One way to provide support is via the collaboration between universities and industries, allowing the co-creation of knowledge that leads to innovation. Historically, universities, as knowledge-intensive organizations (KIOs), have produced knowledge through research. At present, its important contribution to countries’ economy is widely recognized through the development of new knowledge and technical know-how. Universities are a source of innovation for firms, which ultimately translates into social welfare improvements. The objective of this research is to analyze the university–firm linkage. The methodological strategy is carried out using Bayesian networks through a model where the main elements of university–industry linking, which impact competitiveness and innovation, are identified and quantified. The technology transfer model shows that the most crucial processes are Technology Strategy, Value Proposal, Knowledge Management, Control and Monitoring, Innovation Management, Needs Detection, Knowledge Creation, New Products and Services, and Absorption Capacity. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Knowledge Management for Open Innovation: Bayesian Networks through Machine Learning

2021 , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora , Dávila-Aragón, Griselda

Knowledge management within organizations allows to support a global business strategy and represents a systemic and organized attempt to use knowledge within an organization to improve its performance. The objective of this research is to study and analyze knowledge management through Bayesian networks with machine learning techniques, for which a model is made to identify and quantify the various factors that affect the correct management of knowledge in an organization, allowing you to generate value. As a case study, a technology-based services company in Mexico City is analyzed. The evidence found shows the optimal and non-optimal management of knowledge management, and its various factors, through the causality of the variables, allowing us to more adequately capture the interrelationship to manage it. The results show that the most relevant factors for having adequate knowledge management are information management, relational capital, intellectual capital, quality and risk management, and technology assimilation. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.

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Machine Learning Approach for Pre-Eclampsia Risk Factors Association

2018 , Martínez Velasco, Antonieta Teodora , Martinez-Villaseñor, Lourdes , Miralles-Pechuán, Luis

The preeclampsia/eclampsia syndrome is a multisystem disorder that usually includes cardiovascular changes, hematologic abnormalities, hepatic and renal impairment, and neurologic or cerebral manifestations. Preeclampsia (PE) is a clinical syndrome that afflicts 3–5% of pregnancies and it is a leading cause of maternal mortality, especially in developing countries. To understand in greater depth the preeclampsia/eclampsia syndrome, we applied some well-known Machine Learning (ML) techniques. ML has been successfully applied to medical research to improve the diagnosis and the prevention of complex diseases and syndromes. In our contribution, we have created a supervised model to predict if a patient suffers the disease. This model has been optimized by selecting the best features and by optimizing the threshold when predicting a class. We used these techniques to point out the most related features of the patients to the disease. Finally, we used interpretability techniques to extract and visualize through a decision tree the most relevant associations of the disease with the patients' features. © 2018 Association for Computing Machinery.

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Metodología para determinar los factores de riesgo asociados con enfermedades complejas: Degeneración Macular Relacionada con la Edad y Preeclampsia

2021 , Martínez Velasco, Antonieta Teodora , Martinez-Villaseñor, Lourdes , Estrada Mena, Francisco Javier , Campus Ciudad de México

El incremento en la aplicación de la inteligencia artificial en la creación de sistemas de soporte de decisiones a escala está transformando también el futuro del cuidado de la salud. La inteligencia artificial se ha utilizado para implementar sistemas de diagnóstico y pronóstico de enfermedades, optimización del tratamiento y predicción del resultado, desarrollo de fármacos y para lidiar con problemas de salud pública. Los datos provenientes de los pacientes se pueden obtener de los registros médicos; estos generalmente son colecciones complejas de datos. Así, la determinación de los factores de riesgo es un reto importante debido a la gran cantidad de datos que actualmente se generan a partir de los estudios genéticos y datos clínicos obtenidos en la consulta médica de algunos hospitales. Con el fin de atender estos retos, en este trabajo se presenta una metodología para determinar los factores de riesgo asociados a enfermedades complejas mediante el enfoque de aprendizaje automático. La metodología se probó en dos escenarios de aplicación: Degeneración Macular Relacionada con la Edad (DMRE) y Preeclampsia (PE), con la entrega un sistema de toma de decisiones interpretable.

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Machine Learning Sustainable Competitiveness for Global Recovery

2022 , López-Fernández, Andreé Marie , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora

The unexpected appearance and expansion of the pandemic caused by COVID-19 have shown that both developed and less developed countries need strategic, scientific-technological capacities and an innovation ecosystem to respond quickly to these challenges. The objective of this research is to analyze the potential correlation between competitiveness and sustainable development for a global recovery. To carry out the study, five global indexes were considered: competitiveness, sustainability, innovation, impunity, and human development which were analyzed with a mixed-method approach, quantitative and qualitative analysis. Organizational and government leaders are facing significant collateral effects of the health pandemic including economic recession and social development regression; therefore, the road to recovery requires they work toward sustainable development to reach desired competitiveness. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

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Transformación digital para la competitividad de las empresas

2024 , Jorge Arturo Salgado García , Terán-Bustamante, Antonia , Martínez Velasco, Antonieta Teodora

La transformación digital es un proceso tanto tecnológico como sociocultural que involucra la adopción de tecnologías digitales y modificaciones en los modelos de negocio y la estrategia de las firmas. La literatura que estudia relaciones entre la transformación digital y la competitividad de las empresas se incrementó en el periodo de postpandemia, sin embargo, las investigaciones antes de la pandemia son escasas, por lo cual el objetivo de esta investigación es analizar el efecto de la transformación digital en la competitividad de las empresas antes de esta crisis. Los datos que se utilizaron para el análisis son de las Encuestas Nacionales sobre Productividad y Competitividad de las Pymes en Sectores Estratégicos en México. El análisis se realizó en dos partes: I. geoestadístico para buscar clústeres geográficos de corte natural y II. estadístico mediante la regresión Ridge. Los resultados evidenciaron que tanto la transformación digital como la competitividad se distribuyen de manera desigual en los territorios; sin embargo, se encontró un efecto positivo de la transformación digital en la competitividad de estas. De acuerdo con lo anterior, se concluyó que las empresas que quieran aumentar su competitividad deben incrementar su transformación digital, por lo que los gobiernos deben continuar estableciendo políticas y programas de transformación digital en todos los sectores y en forma más equitativa.

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