Optimización del diagnóstico de la diabetes: un enfoque de análisis no lineal con inteligencia artificial para reducir la incertidumbre en la toma de decisiones

Arias Bodmer, Juan Camilo

Optimización del diagnóstico de la diabetes: un enfoque de análisis no lineal con inteligencia artificial para reducir la incertidumbre en la toma de decisiones

dc.contributor.advisor	Torres Soler, Luis Carlos
dc.contributor.advisor	Ramos Montaña, Jesus David
dc.contributor.author	Arias Bodmer, Juan Camilo
dc.contributor.orcid	Arias Bodmer, Juan Camilo [0009-0001-4401-520X]
dc.date.accessioned	2024-07-17T16:45:09Z
dc.date.available	2024-07-17T16:45:09Z
dc.date.issued	2024-05
dc.description.abstract	En el ámbito de la medicina, la toma de decisiones juega un papel fundamental en la atención sanitaria, tanto a nivel clínico como en la salud poblacional. La complejidad de este proceso, marcado por dimensiones como la identificación de problemas, el acuerdo en protocolos clínicos, el conocimiento necesario y la estructura de pensamiento, resalta la importancia de abordar las decisiones médicas de manera integral y multidimensional. La toma de decisiones diagnósticas y terapéuticas en pacientes con enfermedades crónicas, como la diabetes, conlleva una incertidumbre inherente que desafía a los profesionales de la salud. En este contexto, la integración de tecnologías avanzadas, como el machine learning (ML), en la práctica clínica puede modificar el enfoque determinista basado únicamente en resultados de laboratorio. Este cambio busca considerar la interacción sistémica de múltiples variables en cada paciente. La inclusión de técnicas de análisis no lineal de datos permite una comprensión más holística del paciente, lo que puede llevar a una interpretación más precisa de la variabilidad de la diabetes. Este enfoque integrador y más completo tiene el potencial de mejorar la identificación temprana y el tratamiento de la enfermedad. Este trabajo aborda el desafío del diagnóstico de la diabetes en un contexto de desbalance de datos, utilizando enfoques analíticos similares a los estudios ecológicos, que buscan comprender las interacciones en un sistema complejo, para equilibrar las clases de manera representativa. Se reconoce que existen limitaciones en cuanto al tamaño de la muestra, sesgos potenciales en la selección de variables y la interpretación de resultados, lo que destaca la importancia de abordar de manera transparente las restricciones y desafíos presentes en este tipo de análisis de datos del mundo real. En este caso, se busca equilibrar las clases de pacientes con y sin diabetes para que el análisis sea más preciso y representativo de la realidad. Abordar de forma transparente estas restricciones y desafíos permitirá a otros investigadores comprender y contextualizar adecuadamente los hallazgos. Al considerar las implicaciones de este estudio y las posibles direcciones futuras de investigación, se resalta la relevancia de los modelos de ML en la predicción de la aparición de la diabetes y la mejora de la toma de decisiones clínicas. La validación y comparación de diferentes modelos de ML se presenta como una estrategia clave para fortalecer la planificación de intervenciones en salud poblacional y optimizar la atención a pacientes con diabetes. En este contexto de constante evolución tecnológica y desafíos en la toma de decisiones médicas, este trabajo busca contribuir al conocimiento y la práctica en el campo de la medicina, ofreciendo un enfoque integral y multidimensional para abordar la complejidad de la atención sanitaria y la gestión de enfermedades crónicas como la diabetes.
dc.description.abstractenglish	In the field of medicine, decision making plays a fundamental role in health care, both at the clinical level and in population health. The complexity of this process, marked by dimensions such as problem identification, agreement on clinical protocols, necessary knowledge and thought structure, highlights the importance of approaching medical decisions in a comprehensive and multidimensional manner. Diagnostic and therapeutic decision making in patients with chronic diseases, such as diabetes, carries an inherent uncertainty that challenges healthcare professionals. In this context, the integration of advanced technologies, such as machine learning (ML), into clinical practice can modify the deterministic approach based solely on laboratory results. This shift seeks to consider the systemic interaction of multiple variables in each patient. The inclusion of nonlinear data analysis techniques allows for a more holistic understanding of the patient, which can lead to a more accurate interpretation of diabetes variability. This integrative and more comprehensive approach has the potential to improve early identification and treatment of the disease. This paper addresses the challenge of diabetes diagnosis in a context of data imbalance, using analytical approaches similar to ecological studies, which seek to understand interactions in a complex system, to balance classes in a representative manner. It is recognized that there are limitations in terms of sample size, potential biases in variable selection, and interpretation of results, highlighting the importance of transparently addressing the constraints and challenges present in this type of real-world data analysis. In this case, we seek to balance the classes of patients with and without diabetes to make the analysis more accurate and representative of reality. Transparently addressing these constraints and challenges will allow other researchers to properly understand and contextualize the findings. In considering the implications of this study and possible future research directions, the relevance of ML models in predicting the onset of diabetes and improving clinical decision making is highlighted. Validation and comparison of different ML models is presented as a key strategy to strengthen the planning of population health interventions and optimize care for patients with diabetes. In this context of constant technological evolution and challenges in medical decision making, this work seeks to contribute to knowledge and practice in the field of medicine, offering a comprehensive and multidimensional approach to address the complexity of health care and the management of chronic diseases such as diabetes.
dc.identifier.uri	https://hdl.handle.net/20.500.12495/12681
dc.language.iso	es
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dc.rights	Attribution-NonCommercial-NoDerivatives 4.0 International	en
dc.rights.accessrights	info:eu-repo/semantics/openAccess
dc.rights.accessrights	http://purl.org/coar/access_right/c_abf2
dc.rights.local	Acceso abierto	spa
dc.rights.uri	http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.subject	Análisis no lineal
dc.subject	Sistemas complejos
dc.subject	Práctica clínica
dc.subject	Salud poblacional
dc.subject	Modelos de predicción
dc.subject	Evidencia del mundo real
dc.subject.keywords	Non-linear analysis
dc.subject.keywords	Complex systems
dc.subject.keywords	Clinical practice
dc.subject.keywords	Population health
dc.subject.keywords	Predictive models
dc.subject.keywords	Real-world evidence
dc.title	Optimización del diagnóstico de la diabetes: un enfoque de análisis no lineal con inteligencia artificial para reducir la incertidumbre en la toma de decisiones
dc.title.translated	Optimization of diabetes diagnosis: a nonlinear analysis approach with artificial intelligence to reduce uncertainty in decision making