A DSS for Predicting Lymphoma in Primary Sjogren's Syndrome Patients

被引：1

作者：

Avgoustis, Nikos ^{[1
]}

Exarchos, Themis ^{[1
]}

机构：

[1] Ionian Univ, Dept Informat, Corfu, Greece

来源：

GENEDIS 2020: COMPUTATIONAL BIOLOGY AND BIOINFORMATICS | 2021年 / 1338卷

关键词：

Primary Sjogren's Syndrome; Screening test; Machine Learning;

D O I：

10.1007/978-3-030-78775-2_2

中图分类号：

Q [生物科学];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

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.

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页码：7 / 11

页数：5

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