Diagnóstico Hierárquico em Cardiologia Utilizando um Modelo de Mistura de Especialistas e Conhecimento Prévio de Cardiologistas - Chaves, Diogo; Dutenhefner, Pedro; Pappa, Gisele L.; Ribeiro, Antônio; Paixão, Gabriela; Meira Jr, Wagner (Anais Estendidos do Simpósio Brasileiro de Sistemas Multimídia e Web (WebMedia), 2025)
Although several predictive models exist for identifying cardiovascular disorders in electrocardiograms (ECGs), many disregard the well-established knowledge from medical literature, forcing the models to relearn known patterns. We propose a Mixture of Experts (MoE) model that incorporates cardiologists’ expertise to hierarchically organize the expert models.We classify six physiciandefined electrocardiographic abnormalities, covering both rhythm and conduction disorders. Trained on the CODE-15 dataset, our model outperformed a widely used baseline in the literature for this task, achieving an F1-score of 0.84 compared to 0.77 for the reference model. These results highlight the relevance of architectures guided by medical knowledge and demonstrate significant improvements over established approaches, underscoring the potential of our proposal to advance ECG classification performance.