A systematic literature review on Machine Learning Model evaluation on healthcare applications ; Una revisión sistemática de la literatura sobre la evaluación de Modelos de Aprendizaje Automático en aplicaciones de salud ; Uma revisão sistemática da literatura sobre avaliação de Modelos de Aprendizado de Máquina em aplicações de saúde

Machine Learning (ML) models have been applied to solve problems in various fields, which necessarily involves proper evaluation of models to ensure performance. Once deployed, ML models are subject to performance issues, such as those related to changes in data (drift). This type of issue has prompted efforts in model analysis and maintenance, as well as in continual learning, which seeks the ability to continuously learn from a (continuous) stream of data. Therefore, it's important to understand and develop methodologies that can be used to evaluate ML models, making their use in real-world environments feasible. Amongst current areas of application for ML, one that stands out, in particular, is Machine Learning for Healthcare, especially in conjunction with Software for Decision Support of Medical Applications, which presents specific challenges for the evaluation and monitoring of models, particularly given that incorrect prediction or classification can lead to life-threatening situations. This paper presents a systematic literature review that aims at identifying state-of-the-art techniques for evaluating and maintaining ML models for healthcare in effective use in the real world. ; Los modelos de Aprendizaje Automático (AA) se han aplicado para resolver problemas en diversos campos, lo que implica necesariamente una adecuada evaluación de los modelos para garantizar su rendimiento. Una vez implementados, los modelos de AA están sujetos a problemas de rendimiento, como los relacionados con los cambios en los datos (drift). Este tipo de problema ha motivado esfuerzos en el análisis y mantenimiento de modelos, así como en el aprendizaje continuo, que busca la capacidad de aprender de forma continua a partir de un flujo continuo de datos. Por lo tanto, es importante entender y desarrollar metodologías que puedan ser utilizadas para evaluar modelos de AA, lo que permite su uso en entornos del mundo real. Entre las áreas actuales de aplicación del AA, una que destaca en particular es el Aprendizaje Automático ...