The utilization of Electronic Health Records (EHRs) for clinical risk
prediction is on the rise. However, strict privacy regulations limit access to
comprehensive health records, making it challenging to apply standard machine
learning algorithms in practical real-world scenarios. Previous research has
addressed this data limitation by incorporating medical ontologies and
employing transfer learning methods. In this study, we investigate the
potential of leveraging language models (LMs) as a means to incorporate
supplementary domain knowledge for improving the performance of various
EHR-based risk prediction tasks. Unlike applying LMs to unstructured EHR data
such as clinical notes, this study focuses on using textual descriptions within
structured EHR to make predictions exclusively based on that information. We
extensively compare against previous approaches across various data types and
sizes. We find that employing LMs to represent structured EHRs, such as
diagnostic histories, leads to improved or at least comparable performance in
diverse risk prediction tasks. Furthermore, LM-based approaches offer numerous
advantages, including few-shot learning, the capability to handle previously
unseen medical concepts, and adaptability to various medical vocabularies.
Nevertheless, we underscore, through various experiments, the importance of
being cautious when employing such models, as concerns regarding the
reliability of LMs persist.

利用语言模型（LMs）来代表结构化电子健康记录（EHRs），例如诊断历史，可以在各种风险预测任务中提高性能，同时具有少样本学习、处理未知医学概念和适应各种医学词汇等优势，但在使用这些模型时需要谨慎