Examinando por Autor "Márquez, Gastón"
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Ítem Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset(Multidisciplinary Digital Publishing Institute (MDPI), 0025-02) Ormeño-Arriagada, Pablo; Márquez, Gastón; Araya, David; Rimassa, Carla; Taramasco, CarlaReliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EPIVIGILA system and the Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers from class imbalance. To address this issue, we applied various machine learning algorithms, both with and without sampling methods, and compared them using different classification and diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, and diagnostic odds ratio. Our results showed that applying sampling methods to this dataset improved the metric values and contributed to models with better generalization. Effectively managing imbalanced data is crucial for reliable COVID-19 diagnosis. This study enhances the understanding of how machine learning techniques can improve diagnostic reliability and contribute to better patient outcomes. © 2024 by the authors.Ítem Applying Machine Learning Sampling Techniques to Address Data Imbalance in a Chilean COVID-19 Symptoms and Comorbidities Dataset(Multidisciplinary Digital Publishing Institute (MDPI), 2025-02) Ormeño-Arriagada, Pablo; Márquez, Gastón; Araya, David; Rimassa, Carla; Taramasco, CarlaReliably detecting COVID-19 is critical for diagnosis and disease control. However, imbalanced data in medical datasets pose significant challenges for machine learning models, leading to bias and poor generalization. The dataset obtained from the EPIVIGILA system and the Chilean Epidemiological Surveillance Process contains information on over 6,000,000 patients, but, like many current datasets, it suffers from class imbalance. To address this issue, we applied various machine learning algorithms, both with and without sampling methods, and compared them using different classification and diagnostic metrics such as precision, sensitivity, specificity, likelihood ratio positive, and diagnostic odds ratio. Our results showed that applying sampling methods to this dataset improved the metric values and contributed to models with better generalization. Effectively managing imbalanced data is crucial for reliable COVID-19 diagnosis. This study enhances the understanding of how machine learning techniques can improve diagnostic reliability and contribute to better patient outcomes. © 2024 by the authors.Ítem Barriers and Facilitators of Ambient Assisted Living Systems: A Systematic Literature Review(MDPI, 2023-03) Márquez, Gastón; Taramasco, CarlaAmbient Assisted Living Systems (AALSs) use information and communication technologies to support care for the growing population of older adults. AALSs focus on providing multidimensional support to families, primary care facilities, and patients to improve the quality of life of the elderly. The literature has studied the qualities of AALSs from different perspectives; however, there has been little discussion regarding the operational experience of developing and deploying such systems. This paper presents a literature review based on the PRISMA methodology regarding operational facilitators and barriers of AALSs. This study identified 750 papers, of which 61 were selected. The results indicated that the selected studies mentioned more barriers than facilitators. Both barriers and facilitators concentrate on aspects of developing and configuring the technological infrastructure of AALSs. This study organizes and describes the current literature on the challenges and opportunities regarding the operation of AALSs in practice, which translates into support for practitioners when developing and deploying AALSs. © 2023 by the authors.Ítem Detection of COVID-19 Patients Using Machine Learning Techniques: A Nationwide Chilean Study(MDPI, 2022-07-01) Ormeño, Pablo; Márquez, Gastón; Guerrero Nancuante, Camilo; Taramasco, CarlaEpivigila is a Chilean integrated epidemiological surveillance system with more than 17,000,000 Chilean patient records, making it an essential and unique source of information for the quantitative and qualitative analysis of the COVID-19 pandemic in Chile. Nevertheless, given the extensive volume of data controlled by Epivigila, it is difficult for health professionals to classify vast volumes of data to determine which symptoms and comorbidities are related to infected patients. This paper aims to compare machine learning techniques (such as support-vector machine, decision tree and random forest techniques) to determine whether a patient has COVID-19 or not based on the symptoms and comorbidities reported by Epivigila. From the group of patients with COVID-19, we selected a sample of 10% confirmed patients to execute and evaluate the techniques. We used precision, recall, accuracy, F1-score, and AUC to compare the techniques. The results suggest that the support-vector machine performs better than decision tree and random forest regarding the recall, accuracy, F1-score, and AUC. Machine learning techniques help process and classify large volumes of data more efficiently and effectively, speeding up healthcare decision making. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Ítem Using implementation science to develop and deploy an oncology electronic health record(Elsevier Ltd, 2025-01) Taramasco, Carla; Noel, Rene; Márquez, Gastón; Robles, DiegoThe management of oncology clinical processes involves the efficient management of data using electronic clinical records to effectively monitor and treat oncology patients. As the process of treating and monitoring cancer patients involves multiple stakeholders with differing perspectives, the implementation and deployment of oncology clinical registries represent a significant challenge. In this study, we address this complexity by employing a technique that helps translate implementation strategies into requirement identification methods, which are subsequently disseminated throughout the implementation and deployment phases of health information systems. We applied this technique to develop an electronic health record for the national cancer plan in Chile. The findings indicate that six implementation strategies are essential to addressing stakeholder needs, as well as three requirement identification techniques to describe the underlying problem. Furthermore, a study conducted with 27 stakeholders revealed that the perception of the oncology electronic clinical record has considerable acceptance in three critical functionalities related to the clinical process of oncology patient management. The use of implementation science strategies provides an alternative approach to understanding the underlying problem that stakeholders face when they require healthcare technologies.