Ionian University, Department of Informatics, Corfu, Greece.
Adv Exp Med Biol. 2021;1338:7-11. doi: 10.1007/978-3-030-78775-2_2.
Primary Sjogren's syndrome (pSS) is a chronic autoimmune disease followed by exocrine gland dysfunction. In this work, a web application was developed as a screening test based on a machine learning model that was trained on clinical data and is used to predict lymphoma outcomes in pSS patient. The results of the final model reveal a sensitivity of 100%, accuracy of 82%, and area under the curve of 98% and confirms the importance of C4 value, lymphadenopathy, and rheumatoid factor as prominent lymphoma predictors.
原发性干燥综合征（pSS）是一种慢性自身免疫性疾病，随后会出现外分泌腺功能障碍。在这项工作中，开发了一个基于机器学习模型的网络应用程序作为筛查测试，该模型是使用临床数据进行训练的，用于预测 pSS 患者的淋巴瘤结局。最终模型的结果显示敏感性为 100%，准确性为 82%，曲线下面积为 98%，并证实了 C4 值、淋巴结病和类风湿因子作为显著淋巴瘤预测因子的重要性。
