Clinical foundation model for structured clinical data

摘要 在大数据临床数据时代，丰富的真实世界临床数据源(RWcD)的可用性使 开发针对不同临床事件的预测模型，带来提高效率和 降低医疗保健成本。然而，目前实际使用的模型大多是基于本地数据进行训练的， 引入偏见和缺乏概括性的问题。我们将开发综合方法来高效地 培养高质量的临床基础模型(CFM)，从患者的信息表达中学习 以电子病历或索赔形式的结构化临床数据。具体地说，如何培养能够最大限度地 无论预测模型架构如何，均可提升任何下游预测任务的性能 可用训练数据的大小。在本应用程序中，我们建议1)开发一个灵活的框架来 从异构源获取时间结构化的临床数据元素，并用现有的数据元素丰富它 知识，2)优化基础模型架构和预培训策略，3)发展激励 零/少射击学习的策略，以及4)在多个临床下游任务上评估CFM。

Abstract In the era of big clinical data, the availability of rich real-world clinical data sources (RWcD) enables the development of predictive models for different clinical events, bringing the potential to improve efficiency and lower the cost of health care. However, the currently in-use models in practice are mostly trained on local data, introducing issues of bias and lack of generalizability. We will develop comprehensive methods to efficiently train high-quality clinical foundation model (CFM) that learn informative representations from patients' structured clinical data either in the form of EHR or claims. Specifically, how to train CFM that can maximize the performance boost for any downstream prediction tasks regardless of the predictive model architecture and the size of the available training data. In this application we propose to 1) Develop a flexible framework to intake the temporal structured clinical data elements from heterogenous sources and enrich it with existing knowledge, 2) Optimize the foundation model architecture and pre-training strategy, 3) Develop prompting strategies for zero/few shot learning, and 4) Evaluating CFM on multiple clinical downstream tasks.