Transformers have significantly advanced the modeling of Electronic Health
Records (EHR), yet their deployment in real-world healthcare is limited by
several key challenges. Firstly, the quadratic computational cost and
insufficient context length of these models pose significant obstacles for
hospitals in processing the extensive medical histories typical in EHR data.
Additionally, existing models employ separate finetuning for each clinical
task, complicating maintenance in healthcare environments. Moreover, these
models focus exclusively on either clinical prediction or EHR forecasting,
lacking the flexibility to perform well across both. To overcome these
limitations, we introduce EHRMamba, a robust foundation model built on the
Mamba architecture. EHRMamba can process sequences up to four times longer than
previous models due to its linear computational cost. We also introduce a novel
approach to Multitask Prompted Finetuning (MTF) for EHR data, which enables
EHRMamba to simultaneously learn multiple clinical tasks in a single finetuning
phase, significantly enhancing deployment and cross-task generalization.
Furthermore, our model leverages the HL7 FHIR data standard to simplify
integration into existing hospital systems. Alongside EHRMamba, we open-source
Odyssey, a toolkit designed to support the development and deployment of EHR
foundation models, with an emphasis on data standardization and
interpretability. Our evaluations on the MIMIC-IV dataset demonstrate that
EHRMamba advances state-of-the-art performance across 6 major clinical tasks
and excels in EHR forecasting, marking a significant leap forward in the field.

通过引入 EHRMamba 和多任务提示微调（MTF）方法，此研究提出了一种基于 Mamba 架构的稳健基础模型，该模型具有较低的计算成本和较长的上下文长度，并且能够同时学习多个临床任务，从而在电子健康记录领域取得了重大突破。