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（例如死亡率预测）通常依赖专家意见进行特征选择和调整观察窗口大小。这增加了专家负担，成为开发过程中的瓶颈。我们提出检索增强医疗预测模型（REMed）来应对这些挑战。REMed能够评估无限数量的临床事件，选择相关事件并进行预测。该方法有效消除了手动特征选择的需求，并实现了无限制的观察窗口。我们通过在27个临床任务和两个独立公开EHR数据集队列上的实验验证了这些特性，实验中REMed优于其他旨在处理尽可能多事件的当代架构。值得注意的是，我们发现REMed的偏好与医学专家高度一致。我们期望该方法通过最小化临床医生手动参与的需求，显著加速EHR预测模型的开发进程。