Prediction of diagnosis results of rheumatoid arthritis patients based on autoantibodies and cost-sensitive neural network.
Linyu GengWenqiang QuSen WangJiaqi ChenYang XuWei KongXue XuXuebing FengCheng ZhaoJun LiangHuayong ZhangLingyun SunPublished in: Clinical rheumatology (2022)
In-depth analysis of autoantibodies and reliable early diagnosis based on the neural network could guide specialized physicians to develop different treatment plans to prevent deterioration and enable early treatment with antirheumatic drugs for remission. Key Points • There are differences in the seropositive rate of autoimmune diseases. • This is the first study to use a cost-sensitive neural network model to diagnose RA disease in patients. • The diagnosis effect of the cost-sensitive neural network model is better than a single antibody and combined multiple antibody detection.
Keyphrases
- neural network
- rheumatoid arthritis patients
- disease activity
- systemic lupus erythematosus
- rheumatoid arthritis
- end stage renal disease
- primary care
- ejection fraction
- newly diagnosed
- optical coherence tomography
- peritoneal dialysis
- ankylosing spondylitis
- systemic sclerosis
- ulcerative colitis
- quantum dots
- real time pcr
- sensitive detection