Developing clinical prediction models (e.g., mortality prediction) based on electronic health records (EHRs) typically relies on expert opinion for feature selection and adjusting observation window size. This burdens experts and creates a bottleneck in the development process. We propose Retrieval-Enhanced Medical prediction model (REMed) to address such challenges. REMed can essentially evaluate an unlimited number of clinical events, select the relevant ones, and make predictions. This approach effectively eliminates the need for manual feature selection and enables an unrestricted observation window. We verified these properties through experiments on 27 clinical tasks and two independent cohorts from publicly available EHR datasets, where REMed outperformed other contemporary architectures that aim to handle as many events as possible. Notably, we found that the preferences of REMed align closely with those of medical experts. We expect our approach to significantly expedite the development of EHR prediction models by minimizing clinicians' need for manual involvement.

翻译：基于电子健康记录（EHR）开发临床预测模型（如死亡率预测）通常依赖专家意见进行特征选择和调整观测窗口大小。这加重了专家负担，并成为开发流程中的瓶颈。我们提出检索增强医学预测模型（REMed）以应对此类挑战。REMed能够评估无限数量的临床事件，筛选相关事件并做出预测。该方法有效消除了人工特征选择的需求，并实现了不受限制的观测窗口。我们通过公开EHR数据集中27项临床任务及两个独立队列的实验验证了这些特性，结果表明REMed优于其他旨在尽可能处理多事件的当代架构。值得注意的是，我们发现REMed的偏好与医学专家的偏好高度一致。我们预期该方法能通过最大限度减少临床医生的人工参与需求，显著加速EHR预测模型的开发进程。