Source: Lecture Notes in Computer Science - LNCS. Conference titles: International Conference on Intelligent Data Engineering and Automated Learning - IDEAL. Unidade: ICMC
Subjects: REDES NEURAIS, APRENDIZADO COMPUTACIONAL, ARTRITE REUMATOIDE, BIOMARCADORES, TECNOLOGIAS DA SAÚDE
ABNT
FREIRE, Daniela Lopes et al. Physics-informed neural networks for modelling rheumatoid arthritis progression: a novel approach integrating pathophysiological laws with machine learning. Lecture Notes in Computer Science - LNCS. Cham: Springer. Disponível em: https://doi.org/10.1007/978-3-032-10489-2_44. Acesso em: 04 jan. 2026. , 2026APA
Freire, D. L., Megeto, G., Gessoni, L. D., Fantini, I., Silva, R. D. da, Leão, H., et al. (2026). Physics-informed neural networks for modelling rheumatoid arthritis progression: a novel approach integrating pathophysiological laws with machine learning. Lecture Notes in Computer Science - LNCS. Cham: Springer. doi:10.1007/978-3-032-10489-2_44NLM
Freire DL, Megeto G, Gessoni LD, Fantini I, Silva RD da, Leão H, Pitta MG da R, Carvalho ACP de LF de. Physics-informed neural networks for modelling rheumatoid arthritis progression: a novel approach integrating pathophysiological laws with machine learning [Internet]. Lecture Notes in Computer Science - LNCS. 2026 ; 16239 516-527.[citado 2026 jan. 04 ] Available from: https://doi.org/10.1007/978-3-032-10489-2_44Vancouver
Freire DL, Megeto G, Gessoni LD, Fantini I, Silva RD da, Leão H, Pitta MG da R, Carvalho ACP de LF de. Physics-informed neural networks for modelling rheumatoid arthritis progression: a novel approach integrating pathophysiological laws with machine learning [Internet]. Lecture Notes in Computer Science - LNCS. 2026 ; 16239 516-527.[citado 2026 jan. 04 ] Available from: https://doi.org/10.1007/978-3-032-10489-2_44
