非肌层浸润性膀胱癌 (NMIBC) 的治疗以临床和病理标准的风险分层为指导。本研究旨在开发一种自然语言处理 (NLP) 模型，用于从非结构化电子病历 (EMR) 中回顾性识别高风险 NMIBC 患者，并应用该模型来描述患者和肿瘤特征。我们使用了三个独立的 EMR 衍生数据数据集包括 2011-2020 年诊断为膀胱癌的成年患者，用于 NLP 模型开发和训练 (n = 140)、验证 (n = 697) 以及回顾性队列分析的应用 (n = 4,402)。采用深度学习方法训练NLP对病历术语的识别，识别七种高危NMIBC标准；使用 F1 分数评估模型性能，并根据特征进行加权。然后使用算法将每位患者分类为高风险 NMIBC（是/否）。手动审核的记录是黄金标准。模型训练后，除一个不常见特征（前列腺尿道受累）外，所有特征的 F1 分数均 >0.7。 Ta (0.897) 和 T1 (0.897) 的受试者工作曲线下面积 (AUC) 最高；原位癌的 AUC 最低（CIS；0.617）。对于高危NMIBC分类，阳性预测值为79.4%，阴性预测值为93.2%，假阳性率为8.9%。敏感性和特异性分别为 83.7% 和 91.1%。在748名手动确认为高危NMIBC的患者中，196名（26%）患有CIS（其中19%还患有T1，23%还患有Ta病）； 552 个肿瘤 (74%) 没有相关的 CIS。NLP 模型与基于规则的算法相结合，识别出具有良好性能的高风险 NMIBC，并使未来的工作能够研究高风险的真实治疗模式和临床结果NMIBC。

Treatment of non-muscle-invasive bladder cancer (NMIBC) is guided by risk stratification using clinical and pathologic criteria. This study aimed to develop a natural language processing (NLP) model for identifying patients with high-risk NMIBC retrospectively from unstructured electronic medical records (EMRs) and to apply the model to describe patient and tumor characteristics.We used three independent EMR-derived data sets including adult patients with a bladder cancer diagnosis in 2011-2020 for NLP model development and training (n = 140), validation (n = 697), and application for the retrospective cohort analysis (n = 4,402). Deep learning methods were used to train NLP recognition of medical chart terminology to identify seven high-risk NMIBC criteria; model performance was assessed using the F1 score, weighted across features. An algorithm was then used to classify each patient as high-risk NMIBC (yes/no). Manually reviewed records served as the gold standard.The F1 scores after model training were >0.7 for all but one uncommon feature (prostatic urethral involvement). The highest area under the receiver operating curves (AUC) was observed for Ta (0.897) and T1 (0.897); the lowest AUC was for carcinoma in situ (CIS; 0.617). For high-risk NMIBC classification, positive predictive value was 79.4%, negative predictive value was 93.2%, and false-positive rate was 8.9%. Sensitivity and specificity were 83.7% and 91.1%, respectively. Of 748 patients manually confirmed as having high-risk NMIBC, 196 (26%) had CIS (of whom 19% also had T1 and 23% also had Ta disease); 552 tumors (74%) had no associated CIS.The NLP model, combined with a rule-based algorithm, identified high-risk NMIBC with good performance and will enable future work to study real-world treatment patterns and clinical outcomes for high-risk NMIBC.