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El resguardo del debido proceso en las audiencias telemáticas y su implementación en el proceso penal chileno
(Universidad de Chile, 2024)
Este trabajo desarrolla un análisis en torno al origen e implementación de las audiencias telemáticas en
el proceso penal a propósito de la emergencia sanitaria por COVID-19 a nivel mundial que afectó a
nuestro país entre los años 2020 y 2023. Las...
Las cenizas del arte. Pérdida y sobrevivencia del Centro Arte Alameda
(Universidad de Chile, 2023)
profundidad, presenciales y
telemáticas; conversaciones con testigos; visita de lugares claves, para la observación del entorno
y reconstrucción de las escenas; y el uso de archivos audiovisuales y escritos....
Las niñas del Instituto Nacional: crónica de una transformación en curso
(Universidad de Chile, 2024)
De manera telemática, 250 niñas se integraron en marzo del 2021 a séptimo básico, y recién en septiembre de ese año pudieron ir al colegio de manera presencial. En marzo de 2022, por primera vez, el Instituto Nacional tuvo más mujeres que hombres...
Opciones de cartera de servicios y modelos de atención en el SAMU Metropolitano: policy brief
(Universidad de Chile, 2024)
de operadores y técnicos, los
cuales actúan de forma protocolizada
- Fortalecimiento de plataforma telemática para resolución de problemas sanitarios
- Creación de figura del “voluntario” en APH
- SAMU como líder nacional y territorial en capacitación...
Modelo de negocio para la creación de una empresa dedicada a la producción y comercialización de talleres online sobre temáticas culturales
(Universidad de Chile, 2022)
Cultura Telemática es un proyecto de negocio que consiste en la implementación de un nuevo modelo de negocio en el mercado chileno actual, el cual considera el diseño, la producción y comercialización de talleres online sobre temáticas culturales...
El procedimiento administrativo electrónico en Chile
(Universidad de Chile, 2003)
efectividad de la utilización de las herramientas de la informática y la telemática como apoyo de la gestión pública y en particular el efectivo alcance y efectos del gobierno electrónico como escenario capaz de lograr un empoderamiento de los ciudadanos en...
La formación del contrato de compraventa en Internet : algunas reflexiones sobre el tema
(Universidad de Chile, 1999)
Servicio universal de telecomunicaciones
(Universidad de Chile, 2002)
especial relevancia el que se garantice a todas las personas un acceso igualitario a dicha información. Para ello y atendida la estructura tecnológica de las redes telemáticas se hace estrictamente necesario que se garantice un acceso universal a los...
Detección automatizada de estructuras anatómicas retinales en retinografías digitales
(Universidad de Chile, 2022)
Diagnóstico Automatizado de Retinografías Telemáticas (DART) ha
contribuido a aumentar el control y acceso oportuno de los pacientes a exámenes
oftalmológicos, utilizando métodos de aprendizaje de máquinas, identificando a los
pacientes que presentan signos...
Diabetic Retinopathy (DR) is a progressive microangiopathy characterized by lesions and occlusion of retinal vessels. It is estimated that between 5% to 10% of patients with DR may develop Diabetic Maculopathy (DM), a pathology characterized by alterations in the visual acuity caused by lesions located in the retinal macular area. It should be noted that the macula is located in the center of the retina and correspond to the point of maximum visual discrimination; therefore, retinal lesions in this area are a very relevant factor in the classification of the macular status. In our country, the control of patients with DR is performed yearly by acquiring retinal images or, in its absence, a fundus examination. However, in the last decade a significant gap has been observed in the performance of ophthalmologic care of patients with DR. From 2018 onwards, the Diagnostic Automated Retinography Telematics Diagnostics (DART) system has contributed to increasing the control and timely access of patients to ophthalmological examinations using machine learning methods, identifying patients who present pathological signs with suspected DR and who should be referred to on-site controls with specialists. It must be pointed out, however, that the classification algorithm of the artificial intelligence module of DART system does not include the location and extent of the lesions present in the macular area. Therefore, this thesis proposes to correlate the location of the macular area with the retinal lesions, incorporating a binary classifier of the macular state that contributes to the accuracy of the automatic classification of DR currently performed. Furthermore, obtaining this information will allow the prioritization of cases with MD. The results obtained by the Logistic Regression and Decision Tree algorithms, using the information on the macular position and ground truth lesions (MGLG), allowed us to establish that the macular area and its relationship with retinal lesions are the most relevant factor for the determination of macular status, reaching the performance of 100% in Precision, Recall and F1-Score in the classification of each class of interest (presence or absence of maculopathy). For automatically estimating macular position, the algorithm that achieved the best results among the proposed methods was the U-net machine learning method. A sample of 318 retinographies, reached a mean cumulative error of 0.9 μm distance from the actual macular center or the equivalent of approximately of 1⁄5 of a disc diameter (DD). By combining the previous results, using macular position and lesions estimated by DART, this thesis corroborated the Logistic Regression and Support Vector Machine algorithms as the best performing classifiers, reaching a ROC AUC of 0.9761 and 0.9680 respectively, showing the high feasibility of identifying MD automatically....
Diabetic Retinopathy (DR) is a progressive microangiopathy characterized by lesions and occlusion of retinal vessels. It is estimated that between 5% to 10% of patients with DR may develop Diabetic Maculopathy (DM), a pathology characterized by alterations in the visual acuity caused by lesions located in the retinal macular area. It should be noted that the macula is located in the center of the retina and correspond to the point of maximum visual discrimination; therefore, retinal lesions in this area are a very relevant factor in the classification of the macular status. In our country, the control of patients with DR is performed yearly by acquiring retinal images or, in its absence, a fundus examination. However, in the last decade a significant gap has been observed in the performance of ophthalmologic care of patients with DR. From 2018 onwards, the Diagnostic Automated Retinography Telematics Diagnostics (DART) system has contributed to increasing the control and timely access of patients to ophthalmological examinations using machine learning methods, identifying patients who present pathological signs with suspected DR and who should be referred to on-site controls with specialists. It must be pointed out, however, that the classification algorithm of the artificial intelligence module of DART system does not include the location and extent of the lesions present in the macular area. Therefore, this thesis proposes to correlate the location of the macular area with the retinal lesions, incorporating a binary classifier of the macular state that contributes to the accuracy of the automatic classification of DR currently performed. Furthermore, obtaining this information will allow the prioritization of cases with MD. The results obtained by the Logistic Regression and Decision Tree algorithms, using the information on the macular position and ground truth lesions (MGLG), allowed us to establish that the macular area and its relationship with retinal lesions are the most relevant factor for the determination of macular status, reaching the performance of 100% in Precision, Recall and F1-Score in the classification of each class of interest (presence or absence of maculopathy). For automatically estimating macular position, the algorithm that achieved the best results among the proposed methods was the U-net machine learning method. A sample of 318 retinographies, reached a mean cumulative error of 0.9 μm distance from the actual macular center or the equivalent of approximately of 1⁄5 of a disc diameter (DD). By combining the previous results, using macular position and lesions estimated by DART, this thesis corroborated the Logistic Regression and Support Vector Machine algorithms as the best performing classifiers, reaching a ROC AUC of 0.9761 and 0.9680 respectively, showing the high feasibility of identifying MD automatically....
Pros y contras de la nueva ley de servicios sanitarios rurales, desde un enfoque técnico y práctico aplicado al caso de APRS de la provincia del Limarí
(Universidad de Chile, 2022)
producto del Covid-19, donde
las cuarentenas, limitaciones de aforo, la implementación de modalidades telemáticas para la
coordinación laboral y la fuerte crisis sanitaria sin duda afectaron en gran medida a las comunidades locales. Siendo así que su...