Prediction of Treatment Effect of SLE-ITP Patients Based on Cost-Sensitive Neural Network and Variational Autoencoder

The aim of the study was to examine the factors influencing the therapeutic effect of patients with systemic lupus erythematosus combined with immune thrombocytopenia (SLE-ITP) and develop a prediction model to predict the therapeutic effect of SLE-ITP.

Three hundred twenty-four SLE-ITP patients were retrieved from the electronic health record database of SLE patients in Jiangsu Province according to the latest treatment response criteria for ITP. We adopted the Cox model based on the least absolute shrinkage and selection operator to explore the impact factors affecting patient therapeutic effect, and we developed neural network model to predict therapeutic effect, and in prediction model, cost-sensitivity was introduced to address data category imbalance, and variational autoencoder was used to achieve data augmentation. The performance of each model was evaluated by accuracy and the area under the receiver operator curve.

The results showed that B-lymphocyte count, H-cholesterol level, complement-3 level, anticardiolipin antibody, and so on could be used as predictors of SLE-ITP curative effect, and abnormal levels of alanine transaminase, immunoglobulin A, and apolipoprotein B predicted adverse treatment response. The neural network treatment effect prediction model based on cost-sensitivity and variational autoencoder was better than the traditional classifiers, with an overall accuracy rate closed to 0.9 and a specificity of more than 0.9, which was useful for clinical practice to identify patients at risk of ineffective treatment response and to achieve better individualized management.

By predicting the curative effect of SLE-ITP, the severity of patients can be determined, and then the best treatment strategy can be planned to avoid ineffective treatment.