用医学报告时间序列提升癌症生存风险预测准确率
SigBERT: Combining Narrative Medical Reports and Rough Path Signature Theory for Survival Risk Estimation in Oncology
- 将病历文本转为时序嵌入，再用粗糙路径签名提取动态特征
- 在真实肿瘤数据集上实现0.75的C-index，优于传统方法
- 适合做医疗文本时序分析与生存建模的研究者参考
电子病历（EHR）包含大量可用于机器学习的医疗信息，但现有生存分析方法难以有效处理其序列化文本数据。本文提出SigBERT，一种新型时序生存分析框架，可高效处理每位患者的大量临床报告。SigBERT通过提取并平均词嵌入生成句子嵌入，再利用粗糙路径理论中的签名提取技术，从句子嵌入的时间序列中捕捉复杂的时序动态，生成几何特征。这些特征被输入到带有LASSO正则化的Cox模型中，用于估计个体化风险评分。模型在真实世界肿瘤数据集（Léon Bérard中心语料库）上训练并评估，独立测试集上C-index达0.75（标准差0.014）。该方法融合了序列化医疗数据，显著提升了基于叙述性文本的生存分析性能。
原文摘要 · Abstract (English)
Electronic medical reports (EHR) contain a vast amount of information that can be leveraged for machine learning applications in healthcare. However, existing survival analysis methods often struggle to effectively handle the complexity of textual data, particularly in its sequential form. Here, we propose SigBERT, an innovative temporal survival analysis framework designed to efficiently process a large number of clinical reports per patient. SigBERT processes timestamped medical reports by extracting and averaging word embeddings into sentence embeddings. To capture temporal dynamics from the time series of sentence embedding coordinates, we apply signature extraction from rough path theory to derive geometric features for each patient, which significantly enhance survival model performance by capturing complex temporal dynamics. These features are then integrated into a LASSO-penalized Cox model to estimate patient-specific risk scores. The model was trained and evaluated on a real-world oncology dataset from the Léon Bérard Center corpus, with a C-index score of 0.75 (sd 0.014) on the independent test cohort. SigBERT integrates sequential medical data to enhance risk estimation, advancing narrative-based survival analysis.
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