Electronic health records (EHR) offer unprecedented opportunities for
in-depth clinical phenotyping and prediction of clinical outcomes. Combining
multiple data sources is crucial to generate a complete picture of disease
prevalence, incidence and trajectories. The standard approach to combining
clinical data involves collating clinical terms across different terminology
systems using curated maps, which are often inaccurate and/or incomplete. Here,
we propose sEHR-CE, a novel framework based on transformers to enable
integrated phenotyping and analyses of heterogeneous clinical datasets without
relying on these mappings. We unify clinical terminologies using textual
descriptors of concepts, and represent individuals' EHR as sections of text. We
then fine-tune pre-trained language models to predict disease phenotypes more
accurately than non-text and single terminology approaches. We validate our
approach using primary and secondary care data from the UK Biobank, a
large-scale research study. Finally, we illustrate in a type 2 diabetes use
case how sEHR-CE identifies individuals without diagnosis that share clinical
characteristics with patients.

本文提出了一种基于 Transformers 的新型框架 sEHR-CE，可以在不需要映射的情况下，对异构临床数据集进行集成表型和分析。使用预训练的语言模型来预测疾病表型的准确性高于非文本和单个术语方法，并且用 2 型糖尿病为例说明如何识别没有诊断但具有与患者相似临床特征的个体。

sEHR-CE: 结构化电子病历语言建模，用于高效和可推广的患者队列扩展

sEHR-CE: Language modelling of structured EHR data for efficient and generalizable patient cohort expansion

Clinical phenotyping enables the automatic extraction of clinical conditions
from patient records, which can be beneficial to doctors and clinics worldwide.
However, current state-of-the-art models are mostly applicable to clinical
notes written in English. We therefore investigate cross-lingual knowledge
transfer strategies to execute this task for clinics that do not use the
English language and have a small amount of in-domain data available. We
evaluate these strategies for a Greek and a Spanish clinic leveraging clinical
notes from different clinical domains such as cardiology, oncology and the ICU.
Our results reveal two strategies that outperform the state-of-the-art:
Translation-based methods in combination with domain-specific encoders and
cross-lingual encoders plus adapters. We find that these strategies perform
especially well for classifying rare phenotypes and we advise on which method
to prefer in which situation. Our results show that using multilingual data
overall improves clinical phenotyping models and can compensate for data
sparseness.

研究跨语言知识转移策略用于非英语且领域内数据小的诊所中，证明了基于翻译方法与特定领域编码器相结合以及跨语言编码器和适配器可以超越当前最先进的方法。该研究发现这些策略对于分类罕见表型尤其有效，并建议根据具体情况选择使用哪种方法。结果表明，使用多语言数据可以改善临床表型模型并弥补数据稀缺。