Examinando por Autor "Taramasco, Carla"
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Ítem A Step Forward to Formalize Tailored to Problem Specificity Mathematical Transforms(Frontiers Media S.A., 2022-06) Glaría, Antonio; Salas, Rodrigo; Chabert, Stéren; Roncagliolo, Pablo; Arriola, Alexis; Tapia, Gonzalo; Salinas, Matías; Zepeda, Herman; Taramasco, Carla; Oshinubi, Kayode; Demongeot, JacquesLinear functional analysis historically founded by Fourier and Legendre played a significant role to provide a unified vision of mathematical transformations between vector spaces. The possibility of extending this approach is explored when basis of vector spaces is built Tailored to the Problem Specificity (TPS) and not from the convenience or effectiveness of mathematical calculations. Standardized mathematical transformations, such as Fourier or polynomial transforms, could be extended toward TPS methods, on a basis, which properly encodes specific knowledge about a problem. Transition between methods is illustrated by comparing what happens in conventional Fourier transform with what happened during the development of Jewett Transform, reported in previous articles. The proper use of computational intelligence tools to perform Jewett Transform allowed complexity algorithm optimization, which encourages the search for a general TPS methodology. Copyright © 2022 Glaría, Salas, Chabert, Roncagliolo, Arriola, Tapia, Salinas, Zepeda, Taramasco, Oshinubi and Demongeot.Ítem A Technological Framework to Support Asthma Patient Adherence Using Pictograms(Multidisciplinary Digital Publishing Institute (MDPI), 2024-08) Figueroa, Rosa; Taramasco, Carla; Lagos, María Elena; Martínez, Felipe; Rimassa, Carla; Godoy, Julio; Pino, Esteban; Navarrete, Jean; Pinto, Jose; Nazar, Gabriela; Pérez, Cristhian; Herrera, DanielBackground: Low comprehension and adherence to medical treatment among the elderly directly and negatively affect their health. Many elderly patients forget medical instructions immediately after their appointments, misunderstand them, or fail to recall them altogether. Some identified causes include the short time slots allocated for appointments in the public health system in Chile, the complex terminology used by healthcare professionals, and the stress experienced by patients during appointments. One approach to improving patients’ adherence to medical treatment is to combine written and oral instructions with graphical elements such as pictograms. However, several challenges arise due to the ambiguity of natural language and the need for pictograms to accurately represent various medication combinations, doses, and frequencies. Objective: This study introduces SIMAP (System for Integrating Medical Instructions with Pictograms), a technological framework aimed at enhancing adherence among asthma patients through the delivery of pictograms via a computational system. SIMAP utilizes a collaborative and user-centered methodology, involving health professionals and patients in the construction and validation of its components. Methods: The technological framework presented in this study is composed of three parts. The first two are medical indications and pictograms related to the treatment of the disease. Both components were developed through a comprehensive and iterative methodology that incorporates both qualitative and quantitative approaches. This methodology includes the utilization of focus groups, interviews, paper and online surveys, as well as expert validation, ensuring a robust and thorough development. The core of SIMAP is the technological component that leveraged artificial intelligence methods for natural language processing to analyze, tokenize, and associate words and their context to a set of one or more pictograms, addressing issues such as the ambiguity in the text, the cultural factor that involves many ways of expressing the same indication, and typographical errors in the indications. Results: Firstly, we successfully validated 18 clinical indications along with their respective pictograms. Some of the pictograms were redesigned based on the validation results. However, in the final validation, the comprehension percentages of the pictograms exceeded 70%. Furthermore, we developed a software called SIMAP, which translates medical indications into previously validated pictograms. Our proposed software, SIMAP, achieves a correct mapping rate of 96.69%. Conclusions: SIMAP demonstrates great potential as a technological component for supplementing medical instructions with pictograms when tested in a laboratory setting. The use of artificial intelligence for natural language processing can successfully map medical instructions, both structured and unstructured, into pictograms. This integration of textual instructions and pictograms holds promise for enhancing the comprehension and adherence of elderly patients to their medical indications, thereby improving their long-term health.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 Architecture Assessment of the Chilean Epidemiological Surveillance System for Notifiable Diseases (EPIVIGILA): Qualitative Study(JMIR Publications Inc., 2023) Taramasco, Carla; Rimassa, CarlaBackground: To fulfill their epidemiological vigilance function, authorities require valid, complete, timely, precise, and reliable information. Advancements in new technologies have facilitated public health control through vigilance systems for notifiable diseases; these systems can gather large numbers of simultaneous notifications, process a wide array of data, and deliver updated information in real time to relevant decision-makers. A large worldwide deployment of new information technologies was seen during the COVID-19 pandemic; these technologies proved to be efficient, resourceful tools . Platform developers should seek self-evaluation strategies to optimize functionality or improve the capacity of national vigilance systems. These tools exist in the Latin American region at various development stages, although publications reporting architectural characteristics of these tools are scarce. International publications are more abundant a nd serve as a basis for comparing the standards that need to be met. Objective: This study aimed to assess the architecture of the Chilean epidemiological surveillance system for notifiable diseases (EPIVIGILA), as compared to that of the international systems reported in scientific publications. Methods: A search for scientific publications was conducted to identify systematic reviews that documented the architectural characteristics of disease notification and vigilance systems. EPIVIGILA was compared to other systems from countries in Africa, the Americas, Asia, Europe, and Oceania. Results: The following aspects of the architecture were identified: (1) notification provenance, (2) minimum data set, (3) database users, and (4) data quality control. The notifying organizations, including hospitals, clinics, laboratories, and medical consultation offices, were similar among the 13 countries analyzed; this contrasted with Chile, where the reporting agent is the physician who can belong to an organization. The minimum data set include patient identification, disease data, and general codifications. EPIVIGILA includes all these elements, in addition to symptomatology, hospitalization data, type of medicine and treatment result, and laboratory test types. The database users or data analyzers include public health organizations, research organizations, epidemiological organizations, health organizations or departments, and the Centers for Disease Control and Prevention. Finally, for data quality control, the criteria most often used were completeness, consistency, validity, timeliness, accuracy, and competencies. Conclusions: An efficient notification and vigilance system must be capable of promptly identifying probable risks as well as incidence and prevalence of the diseases under surveillance. EPIVIGILA has been shown to comply with high quality and functionality standards, at the level of developed countries, by achieving total national coverage and by providing timely, trustworthy, and complete information at high-security levels, thus obtaining positive assessment from national and international authorities. © 2023 Universidade Estadual de Londrina. All rights reserved.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 Classification of Center of Mass Acceleration Patterns in Older People with Knee Osteoarthritis and Fear of Falling(MDPI, 2022-10) González Olguín, Arturo; Ramos Rodríguez, Diego; Higueras Córdoba, Francisco; Martínez Rebolledo, Luis; Taramasco, Carla; Robles Cruz, Diego(1) Background: The preoccupation related to the fall, also called fear of falling (FOF) by some authors is of interest in the fields of geriatrics and gerontology because it is related to the risk of falling and subsequent morbidity of falling. This study seeks to classify the acceleration patterns of the center of mass during walking in subjects with mild and moderate knee osteoarthritis (KOA) for three levels of FOF (mild, moderate, and high). (2) Method: Center-of-mass acceleration patterns were recorded in all three planes of motion for a 30-meter walk test. A convolutional neural network (CNN) was implemented for the classification of acceleration signals based on the different levels of FOF (mild, moderate, and high) for two KOA conditions (mild and moderate). (3) Results: For the three levels of FOF to fall and regardless of the degree of KOA, a precision of 0.71 was obtained. For the classification considering the three levels of FOF and only for the mild KOA condition, a precision of 0.72 was obtained. For the classification considering the three levels of FOF and only the moderate KOA condition, a precision of 0.81 was obtained, the same as in the previous case, and finally for the classification for two levels of FOF, a high vs. moderate precision of 0.78 was obtained. For high vs. low, a precision of 0.77 was obtained, and for the moderate vs. low, a precision of 0.8 was obtained. Finally, when considering both KOA conditions, a 0.74 rating was obtained. (4) Conclusions: The classification model based on deep learning (CNN) allows for the adequate discrimination of the acceleration patterns of the moderate class above the low or high FOF. © 2022 by the authors.Ítem Co-design of a mobile app for engaging breast cancer patients in reporting health experiences: Qualitative case study(JMIR Publications Inc., 2023) Taramasco, Carla; Rimassa, Carla; Noël, René; Bravo Storm, María Loreto; Sánchez, CésarBackground: The World Health Organization recommends incorporating patient-reported experience measures and patient-reported outcome measures to ensure care processes. New technologies, such as mobile apps, could help report and monitor patients adverse effects and doubts during treatment. However, engaging patients in the daily use of mobile apps is a challenge that must be addressed in accordance with the needs of people. Objective: We present a qualitative case study documenting the process of identifying the information needs of breast cancer patients and health care professionals during the treatment process in a Chilean cancer institution. The study aims to identify patients information requirements for integration into a mobile app that accompanies patients throughout their treatment while also providing features for reporting adverse symptoms. Methods: We conducted focus groups with breast cancer patients who were undergoing chemotherapy (n=3) or who completed chemotherapy between 3 months and 1 year (n=1). We also surveyed health care professionals (n=9) who were involved in patient care and who belonged to the oncology committee of the cancer center where the study took place. Content analysis was applied to the responses to categorize the information needs and the means to satisfy them. A user interface was designed according to the findings of the focus groups and was assessed by 3 trained information system and user interaction design experts from 2 countries, using heuristic evaluation guidelines for mobile apps. Results: Patients information needs were classified into 4 areas: An overview of the disease, information on treatment and day-To-day affairs, assistance on the normality and abnormality of symptoms during treatment, and symptoms relevant to report. Health care professionals required patients to be provided with information on the administrative and financial process. We noted that the active involvement of the following 4 main actors is required to satisfy the information needs: patients, caregivers, social network moderators, and health professionals. Seven usability guidelines were extracted from the heuristic evaluation recommendations. Conclusions: A mobile app that seeks to accompany breast cancer patients to report symptoms requires the involvement of multiple participants to handle the reports and day-To-day information needs. User interfaces must be designed with consideration of the patient s social conventions and the emotional load of the disease information. © 2023 Journal of Medical Internet Research. All rights reserved.Ítem Desafíos en la vigilancia de todos los casos de cáncer en Chile: Registro Nacional de Cáncer(Medwave Estudios Ltda, 2024-01) Taramasco, Carla; Rimassa, Carla; Acevedo, JohanaEl cáncer causa millones de muertes a nivel mundial por lo que su registro es fundamental, existiendo registros clínicos, hospitalarios y poblacionales. Estos últimos son el estándar de oro para la información sobre incidencia y supervivencia de cáncer en una región definida. En Chile se cuenta con cinco registros poblacionales ubicados en ciertas zonas del país. El Registro Nacional del Cáncer chileno surge como un desafío para conformar una herramienta transversal a los tres tipos de registro con la finalidad de, al menos, conocer la cantidad de casos por tipo de cáncer. Su diseño implicó un despliegue de acciones orientadas a lograr consensos entre diversos actores respecto de la información, validación y eventos necesarios de registrar. Se identificaron cuatro etapas en el proceso de atención y el registro: sospecha de diagnóstico, confirmación morfológica (biopsia), resolución clínica (comité oncológico incluyendo la indicación de tratamiento), tratamiento y seguimiento oncológico. A su vez, el desarrollo de la plataforma (años 2018 a 2021) implicó levantamiento de información y acuerdos sobre los requerimientos para el co-diseño del registro, incluyendo un exitoso pilotaje con más de 20 establecimientos de salud del sector público y privado con registro de cerca de 7500 casos de cáncer. El despliegue y uso del Registro Nacional de Cáncer a nivel nacional depende de la autoridad sanitaria. Se trata de un sistema de información que recolecta, almacena, procesa y analiza de forma continua y sistemática datos sobre todos los casos y tipos de cánceres que ocurren en el país. En este trabajo se presenta el diseño y desarrollo de la herramienta, los desafíos abordados, sus fortalezas y debilidades.Ítem Design of an Electronic Health Record for Treating and Monitoring Oncology Patients in Chile(Institute of Electrical and Electronics Engineers Inc., 2023) Taramasco, Carla; Rivera, Diego; Guerrero, Camilo; Marquez, GastonIdentifying the clinical needs to evaluate and manage the treatment and monitoring of cancer patients is a multidimensional challenge in healthcare institutions. In this regard, electronic health records (EHRs) are beneficial for managing clinical information; however, EHRs focused exclusively on patients with cancer have not been sufficiently adopted. In Chile, the need for oncology EHR has only been briefly addressed, resulting in insufficient updated and systematized information on oncology patients. In this paper, we propose the design of an oncology EHR that manages critical variables and processes for the treatment and monitoring of patients with cancer in Chile. We used a systematic methodology to design a software architecture oriented to focus groups and interviews to elicit the requirements and needs of stakeholders. We created and described an EHR design that considers four modules that group and manage the main variables and processes that are critical for treating and monitoring oncology patients. Enabling and designing a treatment and monitoring registry for cancer patients in Chile is essential because it allows for the evaluation of strategic clinical decisions in favor of patients. © 2013 IEEE.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 Effects of internet-based telemonitoring platforms on the quality of life of oncologic patients: A systematic literature review protocol(Public Library of Science, 2023) Martínez, Felipe; Tobar, Catalina; Taramasco, CarlaIntroduction Telemonitoring involves the transmission of clinical information through digital means, including internet-connected devices such as smartphones, health tracking apps and video conferencing platforms. This strategy could provide a viable alternative to facilitate follow-up in several conditions, including cancer. Objectives To synthesise the available evidence on the effectiveness of internet-based telemonitoring platforms amongst oncological patients. Relevant endpoints include overall quality of life, the ability to detect postoperative complications, severe toxicity reactions attributable to chemotherapy, reducing the frequency of hospitalisations, emergency department visits and mortality. Methods A systematic review of published and unpublished randomised and controlled studies will be carried out. Iterative searches in PubMED/MEDLINE, EMBASE, Epistemonikos, LILACS, and Cochrane CENTRAL repositories from January 2000 to January 2023 will be conducted. Grey literature repositories, such as Clinicaltrials, BioRxiv and MedRxiv will be searched as well. The Cochrane risk of bias tool will be used to assess the quality of the eligible studies. If possible, a meta-analysis based on the random-effects model will be conducted to evaluate changes in any of the aforementioned outcomes. Heterogeneity will be assessed with Cochrane’s Q and I2 statistics. Its exploration will be carried out using subgroup and sensitivity analyses. Relevant subgroups include the proportion of elderly patients in each study, characteristics of each platform, study type, type of funding and moment of conduction (i.e. before or after the COVID-19 pandemic). Publication bias will be assessed using funnel plots and Egger’s test. Copyright: © 2023 Martínez et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.Ítem Effects on Quality of Life of a Telemonitoring Platform amongst Patients with Cancer (EQUALITE): A Randomized Trial Protocol(Multidisciplinary Digital Publishing Institute (MDPI), 2024-04) Martínez, Felipe; Taramasco, Carla; Espinoza, Manuel; Acevedo, Johanna; Goic, Carolina; Nervi, BrunoCancer, a pervasive global health challenge, necessitates chemotherapy or radiotherapy treatments for many prevalent forms. However, traditional follow-up approaches encounter limitations, exacerbated by the recent COVID-19 pandemic. Consequently, telemonitoring has emerged as a promising solution, although its clinical implementation lacks comprehensive evidence. This report depicts the methodology of a randomized trial which aims to investigate whether leveraging a smartphone app called Contigo for disease monitoring enhances self-reported quality of life among patients with various solid cancers compared to standard care. Secondary objectives encompass evaluating the app’s impact on depressive symptoms and assessing adherence to in-person appointments. Randomization will be performed independently using an allocation sequence that will be kept concealed from clinical investigators. Contigo offers two primary functions: monitoring cancer patients’ progress and providing educational content to assist patients in managing common clinical situations related to their disease. The study will assess outcomes such as quality of life changes and depressive symptom development using validated scales, and adherence to in-person appointments. Specific scales include the EuroQol Group’s EQ-5D questionnaire and the Patient Health Questionnaire (PHQ-9). We hypothesize that the use of Contigo will assist and empower patients receiving cancer treatment, which will translate to better quality of life scores and a reduced incidence of depressive symptoms. All analyses will be undertaken with the intention-to-treat principle by a statistician unaware of treatment allocation. This trial is registered in ClinicalTrials under the registration number NCT06086990. © 2024 by the authors.Ítem Evaluation of Machine Learning Techniques for Classifying and Balancing Data on an Unbalanced Mini-Mental State Examination Test Data Collection Applied in Chile(Institute of Electrical and Electronics Engineers Inc., 2024) Ormeno, Pablo; Marquez, Gaston; Taramasco, CarlaThe Mini-Mental State Examination (MMSE) is the most widely used cognitive test for assessing whether suspected symptoms align with cognitive impairment or dementia. The results of this test are meaningful for clinicians but exhibit highly unbalanced distributions in studies and analyses regarding the classification of patients with cognitive impairment. This is a complex problem when a large number of MMSE tests are analysed. Therefore, data balancing and classification techniques are crucial to support decision-making in distinguishing patients with cognitive impairment in an effective and efficient manner. This study explores machine learning techniques for data balancing and classification using a real unbalanced dataset consisting of MMSE test responses collected from 103 elderly patients participating in a Chilean patient monitoring project. We used 8 data classification techniques and five data balancing techniques. We evaluated the performance of the techniques using the following metrics: sensitivity, specificity, F1-score, likelihood ratio (LR+ and LR-), diagnostic odds ratio (DOR), and the area under the ROC curve (AUC). From the set of data balancing and classification techniques used in this study, the results indicate that synthetic minority oversampling and random forest balancing techniques improve the accuracy of cognitive impairment diagnosis. The results obtained in this study support clinical decision-making regarding early classification or exclusion of older adult patients with suspected cognitive impairment. © 2013 IEEE.Ítem How effective are mobile apps in managing people with type 2 diabetes mellitus? A systematic literature review protocol(Public Library of Science, 2024-04-04) Taramasco, Carla; Rimassa, Carla; Garrido, María Elena Lagos; Figueroa, Rosa L.Introduction The rise of new technologies in the field of health is yielding promising results. In certain chronic conditions such as type 2 diabetes mellitus, which ranks among the top five causes of global mortality, it could be useful in supporting patient management. Materials and methods A systematic review will be conducted on scientific publications from the last 5 years (January 2019 to October 2023) to describe the effect of mobile app usage on glycated hemoglobin for the management of adult patients with type 2 diabetes mellitus who participated in randomized controlled clinical trials. The search will be carried out in the databases of MEDLINE (Ovid), Embase (Ovid), CINAHL (EBSCOhost), CENTRAL, WoS, Scopus, Epistemonikos, and LILACS. The search strategy will be constructed using both controlled and natural language. Additionally, the Cochrane filter will be applied to identify randomized controlled trials. The review will include scientific articles reporting studies that present results from randomized controlled trials, with texts in Spanish, English, or French, utilizing mobile applications for the management of adult individuals (over 18 years) with type 2 diabetes mellitus, and whose outcomes report the effects on glycated hemoglobin. The Cochrane Risk of Bias Tool will be used to assess the quality of the studies, and the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) methodology will be implemented to evaluate the certainty of the evidence. Results The analysis will be conducted by observing the value of the glycated hemoglobin levels of the participants. Given that this data is a quantitative and continuous value, it facilitates the identification of the effects of the mobile applications used for the management of type 2 diabetes mellitus (T2DM) in adults. Furthermore, if sufficient data are available, a meta-analysis will be conducted using IBM-SPSS. The effect of the intervention will be estimated by the mean difference. All point estimates will be accompanied by 95% confidence intervals. A random effects model will be used. The heterogeneity of the results will be assessed using Cochrane's Q and I2 statistics. Discussion Considering that the quality of content and functionality of certain applications in the healthcare field is highly variable, it is necessary to evaluate the scientific evidence reported on the effect of the use of this type of technology in people with T2DM.Ítem Implementation experience of an informatic system for the management of hospital beds(NLM (Medline), 2022-12-06) Guerrero Nancuante, Camilo; Taramasco, Carla; Armstrong Barea, LucyThe management of beds within healthcare centers is essential for meeting the health needs of the population. Currently, in Chile there are few computer tools that streamline the functions performed by the Bed Management Units of healthcare centers. The objective of this article is to describe the implementation of a bed management computer system in three hospitals of medium (Modular-La Serena) and high complexity (San José del Carmen-Copiapó y San Juan de Dios-La Serena) of the Chilean public health network. The process used the Framework of dissemination and implementation, which allowed for a consistent flow of bed management, namely: request, allocation of bed, transfer, hospitalization and patient discharge. Likewise, the relevant actors and the minimum variables for the adequate process were identified. The implementation of the system was carried out in stages of validation and configuration of the platform in each healthcare center, user training and follow-up of the start-up. To date, the three hospitals have an operational computer system for managing hospital beds, reporting no difficulties in its use. The next challenge is to carry out a comprehensive evaluation of the impact of the platform, using the indicators agreed upon with the clinical/administrative teams of the health centers. This work is licensed under a Creative Commons Attribution 4.0 International License. La gestión de camas al interior de los centros asistenciales es fundamental para la atención de las necesidades de salud de la población. Actualmente, en Chile se cuenta con escasas herramientas informáticas que agilicen las funciones que realizan las unidades de gestión de camas de los centros asistenciales. El objetivo del presente artículo es describir la implementación de un sistema informático de gestión de camas en tres hospitales de mediana (Modular en La Serena) y alta complejidad (San José del Carmen en Copiapó y San Juan de Dios en La Serena) de la red pública de salud de Chile. El proceso utilizó el de diseminación e implementación, lo que permitió contar con un flujo coherente de gestión de camas, a saber: solicitud, asignación de cama, traslado, hospitalización y egreso de paciente. Asimismo, se identificaron los actores relevantes y las variables mínimas para el adecuado proceso. La implementación del sistema se llevó a cabo en etapas de validación y configuración de la plataforma en cada centro asistencial, capacitaciones a los usuarios y acompañamiento de la puesta en marcha. A la fecha, los tres hospitales cuentan operativamente con el sistema informático de gestión de camas hospitalarias, no reportando dificultades en su uso. El próximo desafío es efectuar una evaluación integral del impacto de la plataforma, utilizando los indicadores acordados con los equipos clínicos/administrativos de los centros de salud.Ítem Improvement in Quality of Life with Use of Ambient-Assisted Living: Clinical Trial with Older Persons in the Chilean Population(MDPI, 2023-01) Taramasco, Carla; Rimassa, Carla; Martinez, FelipeIn Chile, 18% of the population is over 60 years old and is projected to reach 31% in three decades. An aging population demands the development of strategies to improve quality of life (QoL). In this randomized trial, we present the implementation and evaluation of the Quida platform, which consists of a network of unintrusive sensors installed in the houses of elderly participants to monitor their activities and provide assistance. Sixty-nine elderly participants were included. A significant increase in overall QoL was observed amongst participants allocated to the interventional arm (p < 0.02). While some studies point out difficulties monitoring users at home, Quida demonstrates that it is possible to detect presence and movement to identify patterns of behavior in the sample studied, allowing us to visualize the behavior of older adults at different time intervals to support their medical evaluation.Ítem Machine Learning-Based Classification of Sulfide Mineral Spectral Emission in High Temperature Processes(Multidisciplinary Digital Publishing Institute (MDPI), 0025) Toro, Carlos; Díaz, Walter; Reyes, Gonzalo; Peña, Miguel; Caselli, Nicolás; Taramasco, Carla; Ormeño-Arriagada, Pablo; Balladares, EduardoAccurate classification of sulfide minerals during combustion is essential for optimizing pyrometallurgical processes such as flash smelting, where efficient combustion impacts resource utilization, energy efficiency, and emission control. This study presents a deep learning-based approach for classifying visible and near-infrared (VIS-NIR) emission spectra from the combustion of high-grade sulfide minerals. A one-dimensional convolutional neural network (1D-CNN) was developed and trained on experimentally acquired spectral data, achieving a balanced accuracy score of 99.0% in a test set. The optimized deep learning model outperformed conventional machine learning methods, highlighting the effectiveness of deep learning for spectral analysis in high-temperature environments. In addition, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied to enhance model interpretability and identify key spectral regions contributing to classification decisions. The results demonstrated that the model successfully distinguished spectral features associated with different mineral species, offering insights into combustion dynamics. These findings support the potential integration of deep learning for real-time spectral monitoring in industrial flash smelting operations, thereby enabling more precise process control and decision-making. © 2025 by the authors.Ítem Sensitivity and Specificity of Patient-Reported Clinical Manifestations to Diagnose COVID-19 in Adults from a National Database in Chile: A Cross-Sectional Study(MDPI, 2022-08) Martinez, Felipe; Muñoz, Sergio; Guerrero Nancuante, Camilo; Taramasco, Carla(1) Background: The diagnosis of COVID-19 is frequently made on the basis of a suggestive clinical history and the detection of SARS-CoV-2 RNA in respiratory secretions. However, the diagnostic accuracy of clinical features is unknown. (2) Objective: To assess the diagnostic accuracy of patient-reported clinical manifestations to identify cases of COVID-19. (3) Methodology: Cross-sectional study using data from a national registry in Chile. Infection by SARS-CoV-2 was confirmed using RT-PCR in all cases. Anonymised information regarding demographic characteristics and clinical features were assessed using sensitivity, specificity, and diagnostic odds ratios. A multivariable logistic regression model was constructed to combine epidemiological risk factors and clinical features. (4) Results: A total of 2,187,962 observations were available for analyses. Male participants had a mean age of 43.1 ± 17.5 years. The most common complaints within the study were headache (39%), myalgia (32.7%), cough (31.6%), and sore throat (25.7%). The most sensitive features of disease were headache, myalgia, and cough, and the most specific were anosmia and dysgeusia/ageusia. A multivariable model showed a fair diagnostic accuracy, with a ROC AUC of 0.744 (95% CI 0.743–0.746). (5) Discussion: No single clinical feature was able to fully confirm or exclude an infection by SARS-CoV-2. The combination of several demographic and clinical factors had a fair diagnostic accuracy in identifying patients with the disease. This model can help clinicians tailor the probability of COVID-19 and select diagnostic tests appropriate to their setting. © 2022 by the authors.Ítem Spread of epidemic disease on edge-weighted graphs from a database: A case study of covid-19(MDPI, 2021-04) Manríquez, Ronald; Guerrero-Nancuante, Camilo; Martínez, Felipe; Taramasco, CarlaThe understanding of infectious diseases is a priority in the field of public health. This has generated the inclusion of several disciplines and tools that allow for analyzing the dissemination of infectious diseases. The aim of this manuscript is to model the spreading of a disease in a population that is registered in a database. From this database, we obtain an edge-weighted graph. The spreading was modeled with the classic SIR model. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics. More-over, a deterministic approximation is provided. With database COVID-19 from a city in Chile, we analyzed our model with relationship variables between people. We obtained a graph with 3866 vertices and 6,841,470 edges. We fitted the curve of the real data and we have done some simulations on the obtained graph. Our model is adjusted to the spread of the disease. The model proposed with edge-weighted graph allows for identifying the most important variables in the dissemination of epidemics, in this case with real data of COVID-19. This valuable information allows us to also include/understand the networks of dissemination of epidemics diseases as well as the implementation of preventive measures of public health. These findings are important in COVID-19’s pandemic context. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.