Diabetic eye disease is a major cause of blindness worldwide. The ability to monitor relevant clinical trajectories and detect lapses in care is critical to managing the disease and preventing blindness. Alas, much of the information necessary to support these goals is found only in the free text of the electronic medical record. To fill this information gap, we introduce a system for extracting evidence from clinical text of 19 clinical concepts related to diabetic eye disease and inferring relevant attributes for each. In developing this ophthalmology phenotyping system, we are also afforded a unique opportunity to evaluate the effectiveness of clinical language models at adapting to new clinical domains. Across multiple training paradigms, we find that BERT language models pretrained on out-of-distribution clinical data offer no significant improvement over BERT language models pretrained on non-clinical data for our domain. Our study tempers recent claims that language models pretrained on clinical data are necessary for clinical NLP tasks and highlights the importance of not treating clinical language data as a single homogeneous domain.

糖尿病性眼病是全球失明的主要原因之一。从糖尿病性眼病的临床文本中提取证据，推断相关属性，以监测疾病发展并避免失明，这些任务并不需要基于临床数据预训练的语言模型，强调了不将临床语言数据作为单一的同质领域的重要性。

关注临床BERT：研究语言模型在糖尿病视网膜病变表型识别中的推广性能