Simultaneous confidence bands for nonparametric, polynomial-trigonometric regression estimators

The goal of this paper is to present a method for construction of simultaneous Scheff´e confidence bands for nonparametric prediction functions. The family of nonparametric functions studied here are of the polynomial-trigonometric series type, with estimation of the model parameters undertaken in the standard least-squares framework. After the method is set forth the performance of the procedure is studied via Monte-Carlo simulations

A Radial Basis Function Neural Network using biologically plausible activation functions

Este proyecto se centra en el diseño, la implementación y la evaluación de Redes Neuronales de Función de Base Radial (RBFNN), comparando el modelo gaussiano con una nueva versión que utiliza la función de activación Ricker. La forma de esta función ha sido observada en las señales de neuronas de distintas partes del cerebro humano, a menudo produciendo una señal negativa (inhibitoria) conocida como inhibición lateral. Se han desarrollado dos modelos de RBFNN, incorporando técnicas de Machine Learning (ML) y estadística como la regularización L2 y el algoritmo sigest para mejorar su rendimiento. También se implementan técnicas adicionales, como la estimación de un parámetro k sobredimensionado y la AIC backward selection, para mejorar la eficiencia. En este estudio, los modelos desarrollados se prueban con conjuntos de datos de diferente naturaleza, evaluando su rendimiento con datos sintéticos y realistas, y midiendo sus resultados con problemas de varios niveles de ruido y dificultad. Además, también se realiza una comparación de los modelos para observar qué RBFNN funciona mejor en determinadas condiciones, así como para analizar la diferencia en el número de neuronas y el parámetro de suavizado estimado. La evaluación experimental confirma la eficacia de los modelos RBFNN, proporcionando estimaciones precisas y demostrando su adaptabilidad con problemas de dificultad variable. El análisis comparativo revela que el modelo Ricker tiende a exhibir un rendimiento superior en presencia de altos niveles de ruido, mientras que ambos modelos tienen un rendimiento similar en condiciones de bajo ruido. Estos resultados sugieren la potencial influencia de la inhibición lateral, que podría ser explorada en más profundidad en futuros estudios.This project focuses on the design, implementation and evaluation of Radial Basis Function Neural Networks (RBFNN), comparing the gaussian model with a new version using the Ricker Wavelet activation function. The shape of this wavelet has been observed in the signals of neurons from different parts of the human brain, often producing a negative (inhibitory) signal known as lateral inhibition. Two RBFNN models have been developed, incorporating Machine Learning (ML) and statistical techniques such as L2 regularization and the sigest algorithm for improved performance. Additional techniques, such as estimating an oversized k parameter and using AIC backward selection, are implemented to enhance efficiency. In this study, the developed models are tested with datasets of different nature, evaluating their performance with synthetic and realistic data and measuring their results with problems of various levels of noise and difficulty. Furthermore, a comparison of the models is also made in order to observe which RBFNN performs better on certain conditions, as well as to analyze the difference in the number of neurons and the estimated smoothing parameter. The experimental evaluation confirms the effectiveness of the RBFNN models, yielding accurate estimations and demonstrating their adaptability to problems of varying difficulty. Comparative analysis reveals that the Ricker model tends to exhibit superior performance in the presence of high levels of noise, while both models perform similarly under low noise conditions. These results suggest the potential influence of lateral inhibition, which could be explored further in future studies

A Classification Study of Respiratory Syncytial Virus (RSV) Inhibitors by Variable Selection with Random Forest

Experimental pEC50s for 216 selective respiratory syncytial virus (RSV) inhibitors are used to develop classification models as a potential screening tool for a large library of target compounds. Variable selection algorithm coupled with random forests (VS-RF) is used to extract the physicochemical features most relevant to the RSV inhibition. Based on the selected small set of descriptors, four other widely used approaches, i.e., support vector machine (SVM), Gaussian process (GP), linear discriminant analysis (LDA) and k nearest neighbors (kNN) routines are also employed and compared with the VS-RF method in terms of several of rigorous evaluation criteria. The obtained results indicate that the VS-RF model is a powerful tool for classification of RSV inhibitors, producing the highest overall accuracy of 94.34% for the external prediction set, which significantly outperforms the other four methods with the average accuracy of 80.66%. The proposed model with excellent prediction capacity from internal to external quality should be important for screening and optimization of potential RSV inhibitors prior to chemical synthesis in drug development

Sistema web para la administración de atenciones médicas y monitoreo de la unidad de triaje en el hospital nacional Cayetano Heredia

Despite the lack of an appropriate infrastructure, the number of patients in the Emergency Area of Cayetano Heredia National Hospital has grown in the past few years. The Triage Area is a subarea of the Emergency Area, whose purpose is to classify patients according to their priority level through a quick diagnosis, in order to provide an appropriate level of service to critical patients and optimize the use of hospital resources. This article shows the development of a web system for the integration of the Triage Area with the other subareas of the Emergency area, which would allow the provision of consolidated and shared information in real time, thus speeding up attention process and improving the allocation of the resources critical patients need.    El área de Emergencia del hospital nacional Cayetano Heredia no dispone de una infraestructura adecuada; a pesar de ello, la cantidad de pacientes ha crecido en los últimos años: La unidad de Triaje es una de las subáreas de Emergencia, cuya finalidad es clasificar a los pacientes de acuerdo a su nivel de prioridad a través de un diagnóstico rápido, para ofrecer un nivel de servicio adecuado a los pacientes críticos y optimizar los recursos hospitalarios. El presente artículo muestra el desarrollo de un sistema web para la integración de Triaje con las diferentes subáreas que comprenden el área de Emergencia, el cual permitiría brindar información consolidada y compartida en tiempo real, agilizando de esa manera el proceso de atención y una gestión adecuada de los recursos necesarios para los pacientes en estado crítico. &nbsp

Box Drawings for Learning with Imbalanced Data

The vast majority of real world classification problems are imbalanced, meaning there are far fewer data from the class of interest (the positive class) than from other classes. We propose two machine learning algorithms to handle highly imbalanced classification problems. The classifiers constructed by both methods are created as unions of parallel axis rectangles around the positive examples, and thus have the benefit of being interpretable. The first algorithm uses mixed integer programming to optimize a weighted balance between positive and negative class accuracies. Regularization is introduced to improve generalization performance. The second method uses an approximation in order to assist with scalability. Specifically, it follows a \textit{characterize then discriminate} approach, where the positive class is characterized first by boxes, and then each box boundary becomes a separate discriminative classifier. This method has the computational advantages that it can be easily parallelized, and considers only the relevant regions of feature space