Deep learning model to predict lupus nephritis renal flare based on dynamic multivariable time-series data

作者：Siwan Huang, Yinghua Chen, Yanan Song, Kaiyuan Wu, Tiange Chen, Yuan Zhang, Wenxiao Jia, Haitao Zhang, Dandan Liang, Jing Yang, Caihong Zeng, Xiang Li, Zhi-Hong Liu · 发表于：BMJ Open · 年份：2024 · DOI：10.1136/bmjopen-2023-071821 · 被引用次数：12 · 研究领域：Systemic Lupus Erythematosus Research、Machine Learning in Healthcare、Cytomegalovirus and herpesvirus research

OBJECTIVES: To develop an interpretable deep learning model of lupus nephritis (LN) relapse prediction based on dynamic multivariable time-series data. DESIGN: A single-centre, retrospective cohort study in China. SETTING: A Chinese central tertiary hospital. PARTICIPANTS: The cohort study consisted of 1694 LN patients who had been registered in the Nanjing Glomerulonephritis Registry at the National Clinical Research Center of Kidney Diseases, Jinling Hospital from January 1985 to December 2010. METHODS: We developed a deep learning algorithm to predict LN relapse that consists of 59 features, including demographic, clinical, immunological, pathological and therapeutic characteristics that were collected for baseline analysis. A total of 32 227 data points were collected by the sliding window method and randomly divided into training (80%), validation (10%) and testing sets (10%). We developed a deep learning algorithm-based interpretable multivariable long short-term memory model for LN relapse risk prediction considering censored time-series data based on a cohort of 1694 LN patients. A mixture attention mechanism was deployed to capture variable interactions at different time points for estimating the temporal importance of the variables. Model performance was assessed according to C-index (concordance index). RESULTS: The median follow-up time since remission was 4.1 (IQR, 1.7-6.7) years. The interpretable deep learning model based on dynamic multivariable time-series da...