Clostridioides difficile infection (CDI) is a major cause of antibiotic-associated diarrhea and colitis. It is recognized as one of the most significant hospital-acquired infections. Although CDI can develop severe complications and spores of Clostridioides difficile can be transmitted by the fecal-oral route, CDI is occasionally overlooked in clinical settings. Thus, it is necessary to monitor high CDI risk groups, particularly those undergoing antibiotic treatment, to prevent complications and spread. We developed and validated a deep learning-based model to predict the occurrence of CDI within 28 days after starting antibiotic treatment using longitudinal electronic health records. For each patient, timelines of vital signs and laboratory tests with a 35-day monitoring period and a patient information vector consisting of age, sex, comorbidities, and medications were constructed. Our model achieved the prediction performance with an area under the receiver operating characteristic curve of 0.952 (95% CI: 0.932–0.973) in internal validation and 0.972 (95% CI: 0.968–0.975) in external validation. Platelet count and body temperature emerged as the most important features. The risk score, the output value of the model, exhibited a consistent increase in the CDI group, while the risk score in the non-CDI group either maintained its initial value or decreased. Using our CDI prediction model, high-risk patients requiring symptom monitoring can be identified. This could help reduce the underdiagnosis of CDI, thereby decreasing transmission and preventing complications.

艰难梭菌感染（CDI）是抗生素相关性腹泻和结肠炎的主要原因。它被认为是最重要的医院获得性感染之一。尽管 CDI 可能会出现严重的并发症，并且艰难梭菌孢子可以通过粪口途径传播，但 CDI 在临床环境中偶尔会被忽视。因此，有必要监测 CDI 高危人群，特别是接受抗生素治疗的人群，以防止并发症和扩散。我们开发并验证了一种基于深度学习的模型，利用纵向电子健康记录来预测开始抗生素治疗后 28 天内 CDI 的发生。对于每位患者，构建了 35 天监测期的生命体征和实验室检查时间表以及由年龄、性别、合并症和药物组成的患者信息向量。我们的模型实现了预测性能，内部验证中受试者工作特征曲线下面积为 0.952（95% CI：0.932-0.973），外部验证中受试者工作特征曲线下面积为 0.972（95% CI：0.968-0.975）。血小板计数和体温成为最重要的特征。风险评分（模型的输出值）在 CDI 组中表现出一致的增加，而非 CDI 组的风险评分要么保持其初始值，要么下降。使用我们的 CDI 预测模型，可以识别需要症状监测的高危患者。这有助于减少 CDI 的漏诊，从而减少传播并预防并发症。