A systematic literature review on Machine Learning Model evaluation on healthcare applications

• Autor(a) principal: Souza, Cezar Miranda Paula de
• Data de Publicação: 2023
• Outros Autores: Barreto, Cephas Alves da Silveira, Macedo, Lhayana Vieira de, Brito, Bruna Alice Oliveira de, Targino, Victor Vieira, Betcel, Emanuel Costa, Almeida, Fernando Gomes de, Rodrigues, Arthur Andrade Galvíncio, Malaquias, Ramon Santos, Barroca Filho, Itamir de Morais
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Research, Society and Development
• Texto Completo: https://rsdjournal.org/index.php/rsd/article/view/42042
• topic: ML model validation; ML for Healthcare; ML model monitoring.; Validación de modelos de AA; AA para el sector de la salud; Monitoreo de modelos de AA.; Validação de modelos de AM; AM para a área da saúde; Monitoramento de modelos de AM.

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.