Uncovering symptom-lesion associations through machine learning (2025)
Fonte: Journal of Minimally Invasive Gynecology. Unidades: FM, ESALQ
Assuntos: ANÁLISE DE CORRESPONDÊNCIA, APRENDIZADO COMPUTACIONAL, ENDOMETRIOSE, SINTOMAS LOCAIS
ABNT
SILVA, Renato de Santana et al. Uncovering symptom-lesion associations through machine learning. Journal of Minimally Invasive Gynecology, p. 1-7, 2025Tradução . . Disponível em: https://doi.org/10.1016/j.jmig.2025.08.005. Acesso em: 14 out. 2025.APA
Silva, R. de S., Sátiro, R. M., Abrão, H. M., Rocha, T. P., Milani, F., Caldeira, B. T., et al. (2025). Uncovering symptom-lesion associations through machine learning. Journal of Minimally Invasive Gynecology, 1-7. doi:10.1016/j.jmig.2025.08.005NLM
Silva R de S, Sátiro RM, Abrão HM, Rocha TP, Milani F, Caldeira BT, Andres M de P, Abrão MS. Uncovering symptom-lesion associations through machine learning [Internet]. Journal of Minimally Invasive Gynecology. 2025 ; 1-7.[citado 2025 out. 14 ] Available from: https://doi.org/10.1016/j.jmig.2025.08.005Vancouver
Silva R de S, Sátiro RM, Abrão HM, Rocha TP, Milani F, Caldeira BT, Andres M de P, Abrão MS. Uncovering symptom-lesion associations through machine learning [Internet]. Journal of Minimally Invasive Gynecology. 2025 ; 1-7.[citado 2025 out. 14 ] Available from: https://doi.org/10.1016/j.jmig.2025.08.005