RESUMO
En el artículo anterior se introdujo el tema y se desarrolló cómo es la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje automático supervisados y los métodos de validación interna que permiten corroborar si el modelo arroja resultados similares a los de otros conjuntos de entrenamiento y de prueba. En este artículo continuaremos con la descripción de la evaluación del rendimiento, la selección del modelo más adecuado para identificar la característica que se va a evaluar y la validación externa del modelo. Además, el artículo resume los desafíos existentes en la implementación del Machine Learning desde la investigación al uso clínico. (AU)
In the previous article, we introduced topics such as data collection and analysis, selection and training of supervised machine learning models and methods of internal validation that allow to corroborate whether the model yields similar results to other training and test sets.In this article, we will continue with the description of the performance evaluation, selecting the most appropriate model to identify the characteristic to evaluate and the external validation of the model. In addition, the article summarizes the actual challenges in the implementation of machine learning from research to clinical use. (AU)
Assuntos
Humanos , Modelos Educacionais , Benchmarking/métodos , Aprendizado de Máquina , Tecnologia Biomédica/métodos , Gestão de Ciência, Tecnologia e Inovação em SaúdeRESUMO
Este será el primero de dos artículos donde se tratarán los pasos necesarios para desarrollar un proyecto de aplicación de técnicas de Machine Learning en Salud, que introduce nociones sobre la recolección y análisis de datos, la selección y entrenamiento de modelos de aprendizaje auto-mático de tipo supervisado y los métodos de validación interna para cada modelo. (AU)
This will be the first of two articles where the steps needed to apply machine learning methods in healthcare will be discussed. It will introduce fundamental notions about data collection, selection and training of supervised ML models as well as the methods of internal validation. In a second article, we will discuss about the performance evaluation to select the most appropriate model and its external validation. (AU)
Assuntos
Modelos Educacionais , Gestão de Ciência, Tecnologia e Inovação em Saúde , Aprendizado de Máquina , Algoritmos , Coleta de Dados/métodos , Análise de DadosRESUMO
This study was designed to analyze the effect of hippocampal neurogenesis on the spatial maps of granule cells. Accordingly, we developed and improved an artificial neural network that was originally proposed by Aimone. Many biological processes were included in this revised model to improve the biological relevance of the results. We proposed a novel learning-testing protocol to analyze the activation of encoding place cells across contexts and over time in the dentate gyrus. We observed that, regardless of the presence of neurogenesis, the quantity and morphology of the place fields were represented in the same manner by granule cells. Additionally, we observed that neurogenesis was an effective mechanism for reducing the degree of rate remapping that occurred in the place fields of the granule cells.
