Examinando por Autor "Guerrero Nancuante, Camilo"
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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 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 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.