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。