Development and Verify of Survival Analysis Models for Chinese Patients With Systemic Lupus Erythematosus

BackgroundThe aim of this study is to develop survival analysis models of hospitalized systemic lupus erythematosus (h-SLE) patients in Jiangsu province using data mining techniques to predict patient survival outcomes and survival status. MethodsIn this study, based on 1999-2009 survival data of 2453 hospitalized SLE (h-SLE) patients in Jiangsu Province, we not only used the Cox proportional hazards model to analyze patients' survival factors, but also used neural network models to predict survival outcomes. We used semi-supervised learning to label the censored data and introduced cost-sensitivity to achieve data augmentation, addressing category imbalance and pseudo label credibility. In addition, the risk score model was developed by logistic regression. ResultsThe overall accuracy of the survival outcome prediction model exceeded 0.7, and the sensitivity was close to 0.8, and through the comparative analysis of multiple indicators, our model outperformed traditional classifiers. The developed survival risk assessment model based on logistic regression found that there was a clear threshold, i.e., a survival threshold indicating the survival risk of patients, and cardiopulmonary and neuropsychiatric involvement, abnormal blood urea nitrogen levels and alanine aminotransferase level had the greatest impact on patient survival time. In addition, the study developed a graphical user interface (GUI) integrating survival analysis models to assist physicians in diagnosis and treatment. ConclusionsThe proposed survival analysis scheme identifies disease-related pathogenic and prognosis factors, and has the potential to improve the effectiveness of clinical interventions.

Automated summary

This study developed survival analysis models using data mining techniques to predict the survival outcomes and status of hospitalized systemic lupus erythematosus patients in Jiangsu province. Cox proportional hazards model and neural network models were used for analysis and prediction, and semi-supervised learning and cost-sensitivity were employed to address data imbalance and pseudo label credibility. The study identified the impact of cardiopulmonary and neuropsychiatric involvement, abnormal blood urea nitrogen levels, and alanine aminotransferase level on patient survival time, and developed a graphical user interface to assist physicians in diagnosis and treatment.