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.

翻译：在当前的临床医学领域，传统复发事件分析方法在处理复杂时间依赖性数据时存在局限性。本研究将长短期记忆网络与Cox模型相结合，以提升模型在分析具有动态时间信息的复发事件时的性能。相较于经典模型，LSTM-Cox模型显著提高了临床风险特征提取的准确性，并展现出更低的赤池信息量准则值，同时在模拟数据集上保持了良好性能。在对膀胱癌复发数据的实证分析中，该模型成功降低了训练阶段的均方误差，并在测试集上取得了高达0.90的一致性指数。此外，该模型有效区分了高风险与低风险患者群体，所识别的肿瘤复发次数、最大尺寸等复发风险特征与其他研究及临床试验结果一致。本研究不仅为分析复发数据和提取特征提供了直接高效的方法，也为深度学习技术融入临床风险预测系统提供了便捷途径。