In the current field of clinical medicine, traditional methods for analyzing recurrent events have limitations when dealing with complex time-dependent data. This study combines Long Short-Term Memory networks (LSTM) with the Cox model to enhance the model's performance in analyzing recurrent events with dynamic temporal information. Compared to classical models, the LSTM-Cox model significantly improves the accuracy of extracting clinical risk features and exhibits lower Akaike Information Criterion (AIC) values, while maintaining good performance on simulated datasets. In an empirical analysis of bladder cancer recurrence data, the model successfully reduced the mean squared error during the training phase and achieved a Concordance index of up to 0.90 on the test set. Furthermore, the model effectively distinguished between high and low-risk patient groups, and the identified recurrence risk features such as the number of tumor recurrences and maximum size were consistent with other research and clinical trial results. This study not only provides a straightforward and efficient method for analyzing recurrent data and extracting features but also offers a convenient pathway for integrating deep learning techniques into clinical risk prediction systems.

通过结合长短期记忆网络（LSTM）和Cox模型分析复发事件中动态时间信息以增强模型性能，本研究在临床风险特征提取方面显著提高准确性，同时在模拟数据集上保持良好性能，有效地区分高低风险患者组，在实际膀胱癌复发数据上取得了较高的一致性指数，并得出最大肿瘤数量和大小特征与其他研究和临床试验结果一致，为分析复发数据、提取特征和将深度学习技术整合入临床风险预测系统提供了简易高效的途径。

LSTM-COX模型：处理循环事件的简洁高效深度学习方法