INTRODUCTION Screening for Barrett's esophagus (BE) is suggested in those with risk factors, but remains underutilized. BE/esophageal adenocarcinoma (EAC) risk prediction tools integrating multiple risk factors have been described. However, accuracy remains modest (area under the receiver-operating curve [AUROC] ≤0.7), and clinical implementation has been challenging. We aimed to develop machine learning (ML) BE/EAC risk prediction models from an electronic health record (EHR) database. METHODS The Clinical Data Analytics Platform, a deidentified EHR database of 6 million Mayo Clinic patients, was used to predict BE and EAC risk. BE and EAC cases and controls were identified using International Classification of Diseases codes and augmented curation (natural language processing) techniques applied to clinical, endoscopy, laboratory, and pathology notes. Cases were propensity score matched to 5 independent randomly selected control groups. An ensemble transformer-based ML model architecture was used to develop predictive models. RESULTS We identified 8,476 BE cases, 1,539 EAC cases, and 252,276 controls. The BE ML transformer model had an overall sensitivity, specificity, and AUROC of 76%, 76%, and 0.84, respectively. The EAC ML transformer model had an overall sensitivity, specificity, and AUROC of 84%, 70%, and 0.84, respectively. Predictors of BE and EAC included conventional risk factors and additional novel factors, such as coronary artery disease, serum triglycerides, and electrolytes. DISCUSSION ML models developed on an EHR database can predict incident BE and EAC risk with improved accuracy compared with conventional risk factor-based risk scores. Such a model may enable effective implementation of a minimally invasive screening technology.

中文翻译：

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开发基于电子健康记录的机器学习模型来预测巴雷特食管和食管腺癌的风险。

简介 对于有危险因素的患者，建议进行巴雷特食管 (BE) 筛查，但仍未得到充分利用。已经描述了整合多种风险因素的 BE/食管腺癌 (EAC) 风险预测工具。然而，准确性仍然较低（受试者工作曲线下面积 [AUROC] ≤0.7），并且临床实施一直具有挑战性。我们的目标是根据电子健康记录 (EHR) 数据库开发机器学习 (ML) BE/EAC 风险预测模型。方法 临床数据分析平台是一个包含 600 万 Mayo Clinic 患者的去识别化 EHR 数据库，用于预测 BE 和 EAC 风险。BE 和 EAC 病例和对照是使用国际疾病分类代码和应用于临床、内窥镜检查、实验室和病理记录的增强管理（自然语言处理）技术来识别的。病例的倾向评分与 5 个独立随机选择的对照组相匹配。基于集成变压器的 ML 模型架构用于开发预测模型。结果 我们确定了 8,476 例 BE 病例、1,539 例 EAC 病例和 252,276 例对照。BE ML Transformer 模型的总体敏感性、特异性和 AUROC 分别为 76%、76% 和 0.84。EAC ML Transformer 模型的总体敏感性、特异性和 AUROC 分别为 84%、70% 和 0.84。BE 和 EAC 的预测因素包括传统危险因素和其他新因素，例如冠状动脉疾病、血清甘油三酯和电解质。讨论 与传统的基于风险因素的风险评分相比，在 EHR 数据库上开发的 ML 模型可以预测事件 BE 和 EAC 风险，准确性更高。这样的模型可以有效实施微创筛查技术。